Root Cause Analysis_v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4f28141b",
   "metadata": {},
   "source": [
    "# Root Cause Analysis - PyRCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bc2774",
   "metadata": {},
   "source": [
    "## Import all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ac0df1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Francisco\\anaconda3\\envs\\thesis\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyrca\n",
    "\n",
    "from pyrca.analyzers.ht import HT, HTConfig\n",
    "from pyrca.analyzers.epsilon_diagnosis import EpsilonDiagnosis, EpsilonDiagnosisConfig\n",
    "from pyrca.analyzers.bayesian import BayesianNetwork, BayesianNetworkConfig\n",
    "from pyrca.analyzers.random_walk import RandomWalk, RandomWalkConfig\n",
    "from pyrca.analyzers.rcd import RCD, RCDConfig\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "\n",
    "# Some functions and libraries throw warnings\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4303a36a",
   "metadata": {},
   "source": [
    "## Custom Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c164508",
   "metadata": {},
   "source": [
    "### Data and Directory Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5e006e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_save(G_graph,pos,node_colors,file_name:str, inter_type:str):\n",
    "    test = 'test' # no drawings\n",
    "    plt.figure(figsize=(12, 10))\n",
    "    plt.xlim((-12,14))\n",
    "    plt.ylim((-12,8))\n",
    "    plt.title(f'Causal Graph: {inter_type}', fontsize=12)\n",
    "    nx.draw(G_graph, pos,with_labels=True,node_size=2000, node_color=[node_colors[node] for node in G_graph.nodes()], font_size=6, arrowsize=8,width=0.5)\n",
    "    # plt.savefig(file_name)\n",
    "    # nx.write_gml(G_graph, f'{file_name[:-4]}.gml')\n",
    "\n",
    "def get_file_name(var:str):\n",
    "    return var[7:-4]\n",
    "\n",
    "def create_data_from_list(files):\n",
    "    data_df = pd.DataFrame()\n",
    "    for file in files:\n",
    "        to_join_df = file\n",
    "        data_df = pd.concat([data_df,to_join_df.loc[to_join_df.index]],ignore_index=True)\n",
    "    return data_df\n",
    "\n",
    "def create_train_data(files,startrow):\n",
    "    data_df = pd.DataFrame()\n",
    "    for file_key, file_path in files.items():\n",
    "        to_join_df =  pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "        data_df = pd.concat([data_df,to_join_df.loc[to_join_df.index]],ignore_index=True)\n",
    "    return data_df\n",
    "\n",
    "def get_from_dir(directory_path):\n",
    "    # Get a list of all items (files and directories) in the specified path\n",
    "    all_items = os.listdir(directory_path)\n",
    "    # Iterate over each item and check if it's a directory\n",
    "    for item in all_items:\n",
    "        folder_path = os.path.join(directory_path, item)\n",
    "        if os.path.isdir(folder_path):\n",
    "            print(f\"\\nProcessing folder: {item}\")\n",
    "            folder_contents = os.listdir(folder_path)\n",
    "            for file in folder_contents:\n",
    "                file_path = os.path.join(folder_path, file)\n",
    "                print(f\"    -> {file}\")\n",
    "\n",
    "def get_from_folders(directory_path):\n",
    "    files = {}\n",
    "    all_folders = os.listdir(directory_path)\n",
    "    folder_counter = 0\n",
    "    file_counter = 0\n",
    "    for folder in all_folders:\n",
    "        folder_path = os.path.join(directory_path, folder)\n",
    "        if os.path.isdir(folder_path):\n",
    "            folder_contents = os.listdir(folder_path)\n",
    "            folder_counter +=1\n",
    "            for file in folder_contents:\n",
    "                file_path = os.path.join(folder_path, file)\n",
    "                if file[0:4] == 'data' and file[7:12] != 'event':\n",
    "                    if file[7:13] != 'normal':\n",
    "                        file_counter += 1\n",
    "                        files[f\"{get_file_name(file)}-{folder_counter}.{file_counter}\"] = file_path\n",
    "                    else:\n",
    "                        files[f\"{get_file_name(file)}-{folder_counter}\"] = file_path\n",
    "    \n",
    "    return files, directory_path\n",
    "\n",
    "def results_top_3(results,abnormal_set):\n",
    "    result_columns = results.columns.to_list()\n",
    "    result_index = results.index.to_list()\n",
    "\n",
    "    result_data_3top = pd.DataFrame(columns=result_index, index=result_columns)\n",
    "\n",
    "    for col in result_columns:\n",
    "        for ind in result_index:\n",
    "            for elem in results[col][ind]:\n",
    "                if elem in abnormal_set[col.rsplit('-', 1)[0]].to_list():\n",
    "                    result_data_3top[ind][col] = 1\n",
    "\n",
    "    result_data_3top = result_data_3top.fillna(0)\n",
    "\n",
    "    total_hit = (result_data_3top.sum('index')/len(result_columns)).to_dict()\n",
    "    result_data_3top = pd.concat([result_data_3top,pd.DataFrame(total_hit,index=['Total'])])\n",
    "\n",
    "    return result_data_3top\n",
    "\n",
    "def results_top_1(results,abnormal_set):\n",
    "    result_columns = results.columns.to_list()\n",
    "    result_index = results.index.to_list()\n",
    "    result_data_1top = pd.DataFrame(columns=result_index, index=result_columns)\n",
    "    for col in result_columns:\n",
    "        for ind in result_index:\n",
    "            try: elem = results[col][ind][0] # In case where RCD did not provide any root causes\n",
    "            except: continue\n",
    "            if elem in abnormal_set[col.rsplit('-', 1)[0]].to_list():\n",
    "                result_data_1top[ind][col] = 1\n",
    "\n",
    "    result_data_1top = result_data_1top.fillna(0)\n",
    "\n",
    "    total_hit = (result_data_1top.sum('index')/len(result_columns)).to_dict()\n",
    "    result_data_1top = pd.concat([result_data_1top,pd.DataFrame(total_hit,index=['Total'])])\n",
    "\n",
    "    return result_data_1top\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf1fc2d",
   "metadata": {},
   "source": [
    "### Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e576a92",
   "metadata": {},
   "source": [
    "#### HT - Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bab84d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function run hypothesis testing algorithm\n",
    "def run_HT(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_overlap(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           overlap_p:float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "           \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "    \n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Replace first N rows with overlap data\n",
    "        data_length = abnormal_data_df.shape[0]\n",
    "        overlap_n = int(np.rint(overlap_p*data_length))\n",
    "        overlap_df = normal_data_df.iloc[-overlap_n:]\n",
    "        abnormal_data_df = abnormal_data_df.iloc[-(data_length-overlap_n):]\n",
    "        \n",
    "        # Overlap of overlap_p timestamps\n",
    "        abnormal_data_df = pd.concat([overlap_df,abnormal_data_df], ignore_index=True)\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_HT.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           size_p:float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:size_n]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_normal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           normal_size_p:float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "    \n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    normal_size_n = int(np.rint(normal_size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:normal_size_n]\n",
    "\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_HT.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_abnormal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           abnormal_size_p:float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "     \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Resize abnormal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "        data_length = 526\n",
    "        abnormal_size_n = int(np.rint(abnormal_size_p*data_length))\n",
    "        # We take the first N timestamps\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:abnormal_size_n]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_HT.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_edges_delete(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           edges_delete_n:int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    edges_to_delete = random.sample(edges_list, edges_delete_n)\n",
    "    edges_list_new = edges_list.copy()\n",
    "\n",
    "    for edge in edges_to_delete:\n",
    "        edges_list_new.remove(edge)\n",
    "\n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list_new)\n",
    "    \n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True,).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_HT.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_HT_normal_data(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           normal_data:str,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "\n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)\n",
    "    \n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files[normal_data], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['HT'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = HT(config=HTConfig(adj_matrix_extended_pd))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            if (abnormal_data_df[node] <100).any(): # Score instead of EoL\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "                results[node] = model.find_root_causes(abnormal_data_df, node, True,).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for node in abnormal_nodes:\n",
    "            rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "            rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "            rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[node][0]['root_cause'])\n",
    "            root_cause_results.append(results[node][1]['root_cause'])\n",
    "            root_cause_results.append(results[node][2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_HT.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_HT.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f406611",
   "metadata": {},
   "source": [
    "#### ED - Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8492ad31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function run epsilon diagnosis algorithm\n",
    "def run_ED(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    normal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_files[file_key] = file_path\n",
    "\n",
    "            # if normal data available\n",
    "            folder_name = 'Results'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    normal_data_df = pd.read_csv(normal_files['normal-1'], skiprows=range(1, startrow))\n",
    "    normal_data_df = normal_data_df[nodes]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['ED'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = EpsilonDiagnosis(EpsilonDiagnosisConfig(alpha=0.05,root_cause_top_k=3,bootstrap_time=200))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any():\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_data_df).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_ED.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "        \n",
    "    return results_out\n",
    "\n",
    "def run_ED_overlap(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           overlap_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results_overlap'\n",
    "            filename = f'{file_key}_ED.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['ED'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Replace first N rows with overlap data\n",
    "        data_length = abnormal_data_df.shape[0]\n",
    "        overlap_n = int(np.rint(overlap_p*data_length))\n",
    "        overlap_df = normal_data_df.iloc[-overlap_n:]\n",
    "        abnormal_data_df = abnormal_data_df.iloc[-(data_length-overlap_n):]\n",
    "        \n",
    "        # Overlap of overlap_p timestamps\n",
    "        abnormal_data_df = pd.concat([overlap_df,abnormal_data_df], ignore_index=True)\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        model = EpsilonDiagnosis(EpsilonDiagnosisConfig(alpha=0.05,root_cause_top_k=3,bootstrap_time=200))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any():\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_data_df).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_ED.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_ED.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_ED_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "                    'gripper_2':'interGripper2',\n",
    "                    'max_Vel_1':'interVeloRob1',\n",
    "                    'max_Vel_2':'interVeloRob2',\n",
    "                    'camera_1':'interCamera1',\n",
    "                    'camera_2':'interCamera2',\n",
    "                    'camera_3':'interCamera3',\n",
    "                    'conveyor_1':'interConveyor1',\n",
    "                    'conveyor_2':'interConveyor2',\n",
    "                    'conveyor_3':'interConveyor3',\n",
    "                    'feeder_1':'interFeeder1',\n",
    "                    'feeder_2':'interFeeder2',\n",
    "                    'feeder_3':'interFeeder3',\n",
    "                    'size_1':'interSize1',\n",
    "                    'size_2':'interSize2',\n",
    "                    'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results_overlap'\n",
    "            filename = f'{file_key}_ED.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "\n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    size_n = int(np.rint(size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:size_n]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['ED'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:size_n]\n",
    "        \n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "        \n",
    "        model = EpsilonDiagnosis(EpsilonDiagnosisConfig(alpha=0.05,root_cause_top_k=3,bootstrap_time=200))\n",
    "        model.train(normal_data_df)\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any():\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_data_df).to_list()\n",
    "        \n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_ED.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_ED.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0cb9a1",
   "metadata": {},
   "source": [
    "#### RW - Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ede17da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function run random walk algorithm\n",
    "def run_RW(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([abnormal_data_df,normal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RW_overlap(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           overlap_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results_overlap'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Replace first N rows with overlap data\n",
    "        data_length = abnormal_data_df.shape[0]\n",
    "        overlap_n = int(np.rint(overlap_p*data_length))\n",
    "        overlap_df = normal_data_df.iloc[-overlap_n:]\n",
    "        abnormal_data_df = abnormal_data_df.iloc[-(data_length-overlap_n):]\n",
    "\n",
    "        # Overlap of overlap_p timestamps\n",
    "        abnormal_data_df = pd.concat([overlap_df,abnormal_data_df], ignore_index=True)\n",
    "\n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([abnormal_data_df,normal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RW_size(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    size_n = int(np.rint(size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:size_n]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:size_n]\n",
    "\n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([abnormal_data_df,normal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RW_normal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           normal_size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "\n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    normal_size_n = int(np.rint(normal_size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:normal_size_n]\n",
    "    \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([abnormal_data_df,normal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RW_abnormal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           abnormal_size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "\n",
    "    \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Resize abnormal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "        data_length = 526\n",
    "        abnormal_size_n = int(np.rint(abnormal_size_p*data_length))\n",
    "        # We take the first N timestamps \n",
    "        abnormal_data_df = abnormal_data_df.iloc[:abnormal_size_n]\n",
    "        \n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([normal_data_df,abnormal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RW_edges_delete(folder_path: str,\n",
    "           files: dict,\n",
    "           #train_file:pd.DataFrame,\n",
    "           startrow: int,\n",
    "           edges_delete_n: int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "\n",
    "    edges_to_delete = random.sample(edges_list, edges_delete_n)\n",
    "    edges_list_new = edges_list.copy()\n",
    "\n",
    "    for edge in edges_to_delete:\n",
    "        edges_list_new.remove(edge)\n",
    "\n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list_new)\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "    \n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path'])\n",
    "\n",
    "    results_out = pd.DataFrame(index = ['RW'])\n",
    "\n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        model = RandomWalk(RandomWalkConfig(graph=adjacency_df,root_cause_top_k=3))\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        for node in key_nodes:\n",
    "            # Basic Anomaly Detection\n",
    "            if (abnormal_data_df[node] <100).any(): # from EoL to score\n",
    "                abnormal_nodes.append(node)\n",
    "                new_colors[node] = 'yellow'\n",
    "\n",
    "        results = model.find_root_causes(abnormal_nodes,create_data_from_list([abnormal_data_df,normal_data_df])).to_list()\n",
    "\n",
    "        rank1_root_cause = []\n",
    "        rank2_root_cause = []\n",
    "        rank3_root_cause = []\n",
    "        \n",
    "\n",
    "        for i in range(0,int(np.trunc(len(results)/3))):\n",
    "            rank1_root_cause.append(results[i]['root_cause'])\n",
    "            rank2_root_cause.append(results[i+1]['root_cause'])\n",
    "            rank3_root_cause.append(results[i+2]['root_cause'])\n",
    "\n",
    "            root_cause_results.append(results[0]['root_cause'])\n",
    "            root_cause_results.append(results[1]['root_cause'])\n",
    "            root_cause_results.append(results[2]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RW.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "\n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "        \n",
    "        filename = f'{file_names[file_counter]}_RW.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e361846d",
   "metadata": {},
   "source": [
    "#### RCD - Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e73cf07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function run Root cause discovery algorithm\n",
    "def run_RCD(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "\n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "                \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path']) \n",
    "    results_out = pd.DataFrame(index = ['RCD'])\n",
    "    \n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            model = RCD(RCDConfig(bins=5,f_node=node,localized=True,k=3)) #alpha_step=0.05,start_alpha=0.001,alpha_limit=0.5\n",
    "            if (abnormal_data_df[node] < 100).any():\n",
    "                error = True\n",
    "                while error:\n",
    "                    abnormal_nodes.append(node)\n",
    "                    new_colors[node] = 'yellow'\n",
    "                    try:\n",
    "                        results[node] = model.find_root_causes(normal_data_df,abnormal_data_df).to_list()                        \n",
    "                        error = False\n",
    "                    except:\n",
    "                        continue\n",
    "\n",
    "            rank1_root_cause = []\n",
    "            rank2_root_cause = []\n",
    "            rank3_root_cause = []\n",
    "\n",
    "            if len(results[node]) == 3:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "                rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "                root_cause_results.append(results[node][2]['root_cause'])\n",
    "            elif len(results[node]) == 2:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "\n",
    "            elif len(results[node]) == 1:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RCD.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "    \n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_RCD.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RCD_overlap(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           overlap_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "\n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results_overlap'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "                \n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path']) \n",
    "    results_out = pd.DataFrame(index = ['RCD'])\n",
    "    \n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Replace first N rows with overlap data\n",
    "        data_length = abnormal_data_df.shape[0]\n",
    "        overlap_n = int(np.rint(overlap_p*data_length))\n",
    "        overlap_df = normal_data_df.iloc[-overlap_n:]\n",
    "        abnormal_data_df = abnormal_data_df.iloc[-(data_length-overlap_n):]\n",
    "        \n",
    "        # Overlap of overlap_p timestamps\n",
    "        abnormal_data_df = pd.concat([overlap_df,abnormal_data_df], ignore_index=True)\n",
    "        \n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            model = RCD(RCDConfig(bins=5,f_node=node,localized=True,k=3)) #alpha_step=0.05,start_alpha=0.001,alpha_limit=0.5\n",
    "            if (abnormal_data_df[node] < 100).any():\n",
    "                error = True\n",
    "                while error:\n",
    "                    abnormal_nodes.append(node)\n",
    "                    new_colors[node] = 'yellow'\n",
    "                    try:\n",
    "                        results[node] = model.find_root_causes(normal_data_df,abnormal_data_df).to_list()                        \n",
    "                        error = False\n",
    "                    except:\n",
    "                        continue\n",
    "\n",
    "            rank1_root_cause = []\n",
    "            rank2_root_cause = []\n",
    "            rank3_root_cause = []\n",
    "\n",
    "            if len(results[node]) == 3:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "                rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "                root_cause_results.append(results[node][2]['root_cause'])\n",
    "            elif len(results[node]) == 2:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "\n",
    "            elif len(results[node]) == 1:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RCD.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "    \n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_RCD.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RCD_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "\n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    size_n = int(np.rint(size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:size_n]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path']) \n",
    "    results_out = pd.DataFrame(index = ['RCD'])\n",
    "    \n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:size_n]\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            model = RCD(RCDConfig(bins=5,f_node=node,localized=True,k=3)) #alpha_step=0.05,start_alpha=0.001,alpha_limit=0.5\n",
    "            if (abnormal_data_df[node] < 100).any():\n",
    "                error = True\n",
    "                while error:\n",
    "                    abnormal_nodes.append(node)\n",
    "                    new_colors[node] = 'yellow'\n",
    "                    try:\n",
    "                        results[node] = model.find_root_causes(normal_data_df,abnormal_data_df).to_list()                        \n",
    "                        error = False\n",
    "                    except:\n",
    "                        continue\n",
    "\n",
    "            rank1_root_cause = []\n",
    "            rank2_root_cause = []\n",
    "            rank3_root_cause = []\n",
    "\n",
    "            if len(results[node]) == 3:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "                rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "                root_cause_results.append(results[node][2]['root_cause'])\n",
    "            elif len(results[node]) == 2:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "\n",
    "            elif len(results[node]) == 1:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RCD.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "    \n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_RCD.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RCD_normal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           normal_size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "\n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "    \n",
    "    # Resize normal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "    data_length = 526\n",
    "    normal_size_n = int(np.rint(normal_size_p*data_length))\n",
    "    # We take the first N timestamps\n",
    "    normal_data_df = normal_data_df.iloc[:normal_size_n]\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path']) \n",
    "    results_out = pd.DataFrame(index = ['RCD'])\n",
    "    \n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            model = RCD(RCDConfig(bins=5,f_node=node,localized=True,k=3)) #alpha_step=0.05,start_alpha=0.001,alpha_limit=0.5\n",
    "            if (abnormal_data_df[node] < 100).any():\n",
    "                error = True\n",
    "                while error:\n",
    "                    abnormal_nodes.append(node)\n",
    "                    new_colors[node] = 'yellow'\n",
    "                    try:\n",
    "                        results[node] = model.find_root_causes(normal_data_df,abnormal_data_df).to_list()                        \n",
    "                        error = False\n",
    "                    except:\n",
    "                        continue\n",
    "\n",
    "            rank1_root_cause = []\n",
    "            rank2_root_cause = []\n",
    "            rank3_root_cause = []\n",
    "\n",
    "            if len(results[node]) == 3:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "                rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "                root_cause_results.append(results[node][2]['root_cause'])\n",
    "            elif len(results[node]) == 2:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "\n",
    "            elif len(results[node]) == 1:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RCD.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "    \n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_RCD.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n",
    "\n",
    "def run_RCD_abnormal_size(folder_path: str,\n",
    "           files: dict,\n",
    "           startrow: int,\n",
    "           abnormal_size_p: float,\n",
    "           nodes: list,\n",
    "           edges_list:list,\n",
    "           key_nodes: list,\n",
    "           colors: dict,\n",
    "           pos: dict):\n",
    "    \n",
    "    G_graph = nx.DiGraph()\n",
    "    G_graph.add_nodes_from(nodes)\n",
    "    G_graph.add_edges_from(edges_list)  # Make sure `edges_list` is defined somewhere\n",
    "\n",
    "    adj_matrix_extended_pd = nx.to_pandas_adjacency(G_graph, nodes)\n",
    "    adj_matrix_extended = nx.adjacency_matrix(G_graph,nodes).todense()\n",
    "    adjacency_df = pd.DataFrame(adj_matrix_extended, index=G_graph.nodes(), columns=G_graph.nodes())\n",
    "    \n",
    "    interventions = {'gripper_1':'interGripper1',\n",
    "         'gripper_2':'interGripper2',\n",
    "         'max_Vel_1':'interVeloRob1',\n",
    "         'max_Vel_2':'interVeloRob2',\n",
    "         'camera_1':'interCamera1',\n",
    "         'camera_2':'interCamera2',\n",
    "         'camera_3':'interCamera3',\n",
    "         'conveyor_1':'interConveyor1',\n",
    "         'conveyor_2':'interConveyor2',\n",
    "         'conveyor_3':'interConveyor3',\n",
    "         'feeder_1':'interFeeder1',\n",
    "         'feeder_2':'interFeeder2',\n",
    "         'feeder_3':'interFeeder3',\n",
    "         'size_1':'interSize1',\n",
    "         'size_2':'interSize2',\n",
    "         'size_3':'interSize3'}\n",
    "\n",
    "    abnormal_files = {}\n",
    "    file_names = []\n",
    "    counter = 0\n",
    "\n",
    "    for file_key, file_path in files.items():\n",
    "        temp_file_key = file_key.rsplit('-', 1)[0]\n",
    "        if temp_file_key == 'normal':\n",
    "            normal_data_df = pd.read_csv(file_path, skiprows=range(1, startrow))\n",
    "            normal_data_df = normal_data_df[nodes]\n",
    "            folder_name = 'Results'\n",
    "            filename = f'{file_key}_HT.png'\n",
    "            path = os.path.join(folder_path, folder_name)\n",
    "            os.makedirs(path, exist_ok=True)\n",
    "            file_name_save = os.path.join(path, filename)\n",
    "            #draw_save(G_graph, pos, colors, file_name_save, file_key)\n",
    "        \n",
    "        if temp_file_key in interventions:\n",
    "            counter += 1\n",
    "            abnormal_files[file_key] = file_path\n",
    "            file_names.append(file_key+'_'+str(counter))\n",
    "\n",
    "    abnormal_paths_df = pd.DataFrame.from_dict(abnormal_files, orient='index', columns=['file_path']) \n",
    "    results_out = pd.DataFrame(index = ['RCD'])\n",
    "    \n",
    "    for file_counter, abnormal_file_path in enumerate(abnormal_paths_df.values.flatten()):\n",
    "        abnormal_data_df = pd.read_csv(abnormal_file_path, skiprows=range(1, startrow))\n",
    "        abnormal_data_df = abnormal_data_df[nodes]\n",
    "\n",
    "        # Resize abnormal data relative to one cycle time - 26.3 equals 526 timestamps\n",
    "        data_length = 526\n",
    "        abnormal_size_n = int(np.rint(abnormal_size_p*data_length))\n",
    "        # We take the first N timestamps\n",
    "        abnormal_data_df = abnormal_data_df.iloc[:abnormal_size_n]\n",
    "\n",
    "        abnormal_nodes = []\n",
    "        new_colors = colors.copy()\n",
    "        root_cause_results = []\n",
    "\n",
    "        results = pd.DataFrame()\n",
    "        for node in key_nodes:\n",
    "            model = RCD(RCDConfig(bins=5,f_node=node,localized=True,k=3)) #alpha_step=0.05,start_alpha=0.001,alpha_limit=0.5\n",
    "            if (abnormal_data_df[node] < 100).any():\n",
    "                error = True\n",
    "                while error:\n",
    "                    abnormal_nodes.append(node)\n",
    "                    new_colors[node] = 'yellow'\n",
    "                    try:\n",
    "                        results[node] = model.find_root_causes(normal_data_df,abnormal_data_df).to_list()                        \n",
    "                        error = False\n",
    "                    except:\n",
    "                        continue\n",
    "\n",
    "            rank1_root_cause = []\n",
    "            rank2_root_cause = []\n",
    "            rank3_root_cause = []\n",
    "\n",
    "            if len(results[node]) == 3:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "                rank3_root_cause.append(results[node][2]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "                root_cause_results.append(results[node][2]['root_cause'])\n",
    "            elif len(results[node]) == 2:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                rank2_root_cause.append(results[node][1]['root_cause'])\n",
    "\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][1]['root_cause'])\n",
    "\n",
    "            elif len(results[node]) == 1:\n",
    "                rank1_root_cause.append(results[node][0]['root_cause'])\n",
    "                root_cause_results.append(results[node][0]['root_cause'])\n",
    "\n",
    "        results_out[file_names[file_counter]] = [root_cause_results]\n",
    "        results_file_name = f'results_{file_names[file_counter]}_RCD.csv'\n",
    "        results_out.to_csv(os.path.join(path, results_file_name), index=False)\n",
    "    \n",
    "        for node in rank1_root_cause:\n",
    "            new_colors[node] = 'red'\n",
    "\n",
    "        for node in rank2_root_cause:\n",
    "            new_colors[node] = 'crimson'\n",
    "\n",
    "        for node in rank3_root_cause:\n",
    "            new_colors[node] = 'lightcoral'\n",
    "\n",
    "        filename = f'{file_names[file_counter]}_RCD.png'\n",
    "\n",
    "        file_name_save = os.path.join(path, filename)\n",
    "\n",
    "        #draw_save(G_graph, pos, new_colors, file_name_save, file_names[file_counter])\n",
    "\n",
    "    return results_out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b2adbc",
   "metadata": {},
   "source": [
    "## Create the Causal Graph (non-lagged)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fb8781",
   "metadata": {},
   "source": [
    "### Graph Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "360ddf57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "134\n"
     ]
    }
   ],
   "source": [
    "nodes = [\n",
    "    'cam_1_X', 'cam_2_X', 'cam_3_X',\n",
    "    'cam_1_Y', 'cam_2_Y', 'cam_3_Y',\n",
    "    'EoL_1_X', 'EoL_2_X', 'EoL_3_X', 'EoL_4_X', 'EoL_5_X', 'EoL_6_X',\n",
    "    'EoL_1_Y', 'EoL_2_Y', 'EoL_3_Y', 'EoL_4_Y', 'EoL_5_Y', 'EoL_6_Y',\n",
    "    'rob_1_1', 'rob_1_2', 'rob_1_3', 'rob_1_4', 'rob_1_maxVel',\n",
    "    'rob_2_1', 'rob_2_2', 'rob_2_3', 'rob_2_4', 'rob_2_maxVel',\n",
    "    'rob_1_supply', 'rob_2_supply',\n",
    "    'rob_1_vacuum', 'rob_2_vacuum',\n",
    "    'con_1','con_2','con_3',\n",
    "    'score'\n",
    "]\n",
    "\n",
    "\n",
    "edges = [\n",
    "    ('cam_1_X', 'rob_2_1'), ('cam_1_Y', 'rob_2_1'),\n",
    "    ('cam_1_X', 'rob_2_2'), ('cam_1_Y', 'rob_2_2'),\n",
    "    ('cam_1_X', 'rob_2_3'), ('cam_1_Y', 'rob_2_3'),\n",
    "    ('cam_1_X', 'rob_2_4'), ('cam_1_Y', 'rob_2_4'),\n",
    "    \n",
    "    ('cam_2_X', 'rob_1_1'), ('cam_2_Y', 'rob_1_1'),\n",
    "    ('cam_2_X', 'rob_1_2'), ('cam_2_Y', 'rob_1_2'),\n",
    "    ('cam_2_X', 'rob_1_3'), ('cam_2_Y', 'rob_1_3'),\n",
    "    ('cam_2_X', 'rob_1_4'), ('cam_2_Y', 'rob_1_4'),\n",
    "    \n",
    "    ('cam_3_X', 'rob_1_1'), ('cam_3_Y', 'rob_1_1'),\n",
    "    ('cam_3_X', 'rob_1_2'), ('cam_3_Y', 'rob_1_2'),\n",
    "    ('cam_3_X', 'rob_1_3'), ('cam_3_Y', 'rob_1_3'),\n",
    "    ('cam_3_X', 'rob_1_4'), ('cam_3_Y', 'rob_1_4'),\n",
    "    \n",
    "    ('rob_1_maxVel', 'rob_1_1'), ('rob_1_maxVel', 'rob_1_2'),\n",
    "    ('rob_1_maxVel', 'rob_1_3'), ('rob_1_maxVel', 'rob_1_4'),\n",
    "    \n",
    "    ('rob_2_maxVel', 'rob_2_1'), ('rob_2_maxVel', 'rob_2_2'),\n",
    "    ('rob_2_maxVel', 'rob_2_3'), ('rob_2_maxVel', 'rob_2_4'),\n",
    "    \n",
    "    ('con_2', 'rob_1_1'), ('con_2', 'rob_1_2'), ('con_2', 'rob_1_3'), ('con_2', 'rob_1_4'),\n",
    "    ('con_3', 'rob_1_1'), ('con_3', 'rob_1_2'), ('con_3', 'rob_1_3'), ('con_3', 'rob_1_4'),\n",
    "\n",
    "    ('con_2', 'rob_2_1'), ('con_2', 'rob_2_2'), ('con_2', 'rob_2_3'), ('con_2', 'rob_2_4'),\n",
    "    ('con_1', 'rob_2_1'), ('con_1', 'rob_2_2'), ('con_1', 'rob_2_3'), ('con_1', 'rob_2_4'),\n",
    "\n",
    "    ('con_2', 'EoL_1_X'), ('con_2', 'EoL_1_Y'),\n",
    "    \n",
    "    ('rob_1_1', 'rob_2_1'), ('rob_1_1', 'rob_2_2'), ('rob_1_1', 'rob_2_3'), ('rob_1_1', 'rob_2_4'),\n",
    "    ('rob_1_2', 'rob_2_1'), ('rob_1_2', 'rob_2_2'), ('rob_1_2', 'rob_2_3'), ('rob_1_2', 'rob_2_4'),\n",
    "    ('rob_1_3', 'rob_2_1'), ('rob_1_3', 'rob_2_2'), ('rob_1_3', 'rob_2_3'), ('rob_1_3', 'rob_2_4'),\n",
    "    ('rob_1_4', 'rob_2_1'), ('rob_1_4', 'rob_2_2'), ('rob_1_4', 'rob_2_3'), ('rob_1_4', 'rob_2_4'),\n",
    "\n",
    "    ('rob_1_supply', 'rob_1_vacuum'), \n",
    "    ('rob_2_supply', 'rob_2_vacuum'),\n",
    "\n",
    "    \n",
    "    ('rob_1_vacuum', 'rob_2_1'), ('rob_1_vacuum', 'rob_2_2'),\n",
    "    ('rob_1_vacuum', 'rob_2_3'), ('rob_1_vacuum', 'rob_2_4'),\n",
    "\n",
    "    ('rob_1_1', 'EoL_2_X'), ('rob_1_2', 'EoL_2_X'),\n",
    "    ('rob_1_3', 'EoL_2_X'), ('rob_1_4', 'EoL_2_X'),\n",
    "    ('rob_1_1', 'EoL_2_Y'), ('rob_1_2', 'EoL_2_Y'),\n",
    "    ('rob_1_3', 'EoL_2_Y'), ('rob_1_4', 'EoL_2_Y'),\n",
    "    \n",
    "    ('rob_2_1', 'EoL_3_X'), ('rob_2_2', 'EoL_3_X'),\n",
    "    ('rob_2_3', 'EoL_3_X'), ('rob_2_4', 'EoL_3_X'),\n",
    "    ('rob_2_1', 'EoL_3_Y'), ('rob_2_2', 'EoL_3_Y'),\n",
    "    ('rob_2_3', 'EoL_3_Y'), ('rob_2_4', 'EoL_3_Y'),\n",
    "    \n",
    "    ('rob_2_1', 'EoL_4_X'), ('rob_2_2', 'EoL_4_X'),\n",
    "    ('rob_2_3', 'EoL_4_X'), ('rob_2_4', 'EoL_4_X'),\n",
    "    ('rob_2_1', 'EoL_4_Y'), ('rob_2_2', 'EoL_4_Y'),\n",
    "    ('rob_2_3', 'EoL_4_Y'), ('rob_2_4', 'EoL_4_Y'),\n",
    "    \n",
    "    ('rob_2_1', 'EoL_5_X'), ('rob_2_2', 'EoL_5_X'),\n",
    "    ('rob_2_3', 'EoL_5_X'), ('rob_2_4', 'EoL_5_X'),\n",
    "    ('rob_2_1', 'EoL_5_Y'), ('rob_2_2', 'EoL_5_Y'),\n",
    "    ('rob_2_3', 'EoL_5_Y'), ('rob_2_4', 'EoL_5_Y'),\n",
    "\n",
    "    ('rob_2_1', 'EoL_6_X'), ('rob_2_2', 'EoL_6_X'),\n",
    "    ('rob_2_3', 'EoL_6_X'), ('rob_2_4', 'EoL_6_X'),\n",
    "    ('rob_2_1', 'EoL_6_Y'), ('rob_2_2', 'EoL_6_Y'),\n",
    "    ('rob_2_3', 'EoL_6_Y'), ('rob_2_4', 'EoL_6_Y'),\n",
    "\n",
    "    ('rob_1_vacuum', 'EoL_2_X'), ('rob_1_vacuum', 'EoL_2_Y'),\n",
    "    \n",
    "    ('rob_2_vacuum', 'EoL_3_X'), ('rob_2_vacuum', 'EoL_3_Y'),\n",
    "    ('rob_2_vacuum', 'EoL_4_X'), ('rob_2_vacuum', 'EoL_4_Y'),\n",
    "    ('rob_2_vacuum', 'EoL_5_X'), ('rob_2_vacuum', 'EoL_5_Y'),\n",
    "    ('rob_2_vacuum', 'EoL_6_X'), ('rob_2_vacuum', 'EoL_6_Y'),\n",
    "\n",
    "    ('EoL_1_X','score'), ('EoL_2_X','score'), ('EoL_3_X','score'), ('EoL_4_X','score'), ('EoL_5_X','score'), ('EoL_6_X','score'),\n",
    "    ('EoL_1_Y','score'), ('EoL_2_Y','score'), ('EoL_3_Y','score'), ('EoL_4_Y','score'), ('EoL_5_Y','score'), ('EoL_6_Y','score')\n",
    "]\n",
    "\n",
    "print(len(edges))\n",
    "\n",
    "check_nodes = ['score']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef6da23",
   "metadata": {},
   "source": [
    "### Additional Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ccef0def",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = {\n",
    "    'cam_1_X':(8,4), 'cam_2_X':(-9,6), 'cam_3_X':(-5,6),\n",
    "    'cam_1_Y':(8,2), 'cam_2_Y':(-7,6), 'cam_3_Y':(-3,6),\n",
    "    'EoL_1_X':(10,-8), 'EoL_2_X':(-10,-8), 'EoL_3_X':(-6,-8), 'EoL_4_X':(-2,-8), 'EoL_5_X':(2,-8), 'EoL_6_X':(6,-8),\n",
    "    'EoL_1_Y':(12,-8), 'EoL_2_Y':(-8,-8), 'EoL_3_Y':(-4,-8), 'EoL_4_Y':(0,-8), 'EoL_5_Y':(4,-8), 'EoL_6_Y':(8,-8),\n",
    "    'score':(0,-10),\n",
    "    'rob_2_1':(-6,-4), 'rob_2_2':(-4,-4), 'rob_2_3':(-2,-4), 'rob_2_4':(-0,-4), 'rob_2_maxVel':(2,-4),\n",
    "    'rob_1_1':(-9,1), 'rob_1_2':(-7,1), 'rob_1_3':(-5,1), 'rob_1_4':(-3,1), 'rob_1_maxVel':(-1,1),\n",
    "    'rob_1_vacuum':(2,1), 'rob_2_vacuum':(5,-4),'rob_1_supply':(5,1), 'rob_2_supply':(8,-4),\n",
    "    'con_1':(8,-1),'con_2':(11,6),'con_3':(3,6)\n",
    "}\n",
    "\n",
    "colors = {\n",
    "    'cam_1_X':'skyblue', 'cam_2_X':'skyblue', 'cam_3_X':'skyblue',\n",
    "    'cam_1_Y':'skyblue', 'cam_2_Y':'skyblue', 'cam_3_Y':'skyblue',\n",
    "    'EoL_1_X':'lightgreen', 'EoL_2_X':'lightgreen', 'EoL_3_X':'lightgreen', 'EoL_4_X':'lightgreen', 'EoL_5_X':'lightgreen', 'EoL_6_X':'lightgreen',\n",
    "    'EoL_1_Y':'lightgreen', 'EoL_2_Y':'lightgreen', 'EoL_3_Y':'lightgreen', 'EoL_4_Y':'lightgreen', 'EoL_5_Y':'lightgreen', 'EoL_6_Y':'lightgreen',\n",
    "    'score':'lightsalmon',\n",
    "    'rob_1_1':'tan', 'rob_1_2':'tan', 'rob_1_3':'tan', 'rob_1_4':'tan', 'rob_1_maxVel':'tan',\n",
    "    'rob_2_1':'tan', 'rob_2_2':'tan', 'rob_2_3':'tan', 'rob_2_4':'tan', 'rob_2_maxVel':'tan',\n",
    "    'rob_1_vacuum':'tan', 'rob_2_vacuum':'tan','rob_1_supply':'tan', 'rob_2_supply':'tan',\n",
    "    'con_1':'lightgrey','con_2':'lightgrey','con_3':'lightgrey'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35c55d6",
   "metadata": {},
   "source": [
    "## Show datasets in folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2778c31e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the directory path\n",
    "directory_path = 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07d1e95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "files,folder_path = get_from_folders(directory_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ad61b595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'normal-1': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_1_normal.csv',\n",
       " 'size_1-1.1': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_2_size_1.csv',\n",
       " 'feeder_3-1.2': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-1.3': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-1.4': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-1.5': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_6_size_1.csv',\n",
       " 'feeder_3-1.6': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-1.7': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-1.8': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-2': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_1_normal.csv',\n",
       " 'size_1-2.9': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_2_size_1.csv',\n",
       " 'feeder_3-2.10': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-2.11': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-2.12': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-2.13': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_6_size_1.csv',\n",
       " 'feeder_3-2.14': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-2.15': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-2.16': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-3': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_1_normal.csv',\n",
       " 'size_1-3.17': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_2_size_1.csv',\n",
       " 'feeder_3-3.18': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-3.19': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-3.20': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-3.21': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_6_size_1.csv',\n",
       " 'feeder_3-3.22': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-3.23': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-3.24': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-4': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_1_normal.csv',\n",
       " 'size_1-4.25': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_2_size_1.csv',\n",
       " 'feeder_3-4.26': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-4.27': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-4.28': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-4.29': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_6_size_1.csv',\n",
       " 'feeder_3-4.30': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-4.31': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-4.32': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-5': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_1_normal.csv',\n",
       " 'size_1-5.33': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_2_size_1.csv',\n",
       " 'feeder_3-5.34': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-5.35': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-5.36': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-5.37': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_6_size_1.csv',\n",
       " 'feeder_3-5.38': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-5.39': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-5.40': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-6': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_1_normal.csv',\n",
       " 'size_1-6.41': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_2_size_1.csv',\n",
       " 'feeder_3-6.42': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-6.43': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-6.44': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-6.45': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_6_size_1.csv',\n",
       " 'feeder_3-6.46': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-6.47': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-6.48': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-7': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_1_normal.csv',\n",
       " 'size_1-7.49': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_2_size_1.csv',\n",
       " 'feeder_3-7.50': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-7.51': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-7.52': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-7.53': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_6_size_1.csv',\n",
       " 'feeder_3-7.54': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-7.55': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-7.56': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_9_max_Vel_2.csv',\n",
       " 'normal-8': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_1_normal.csv',\n",
       " 'size_1-8.57': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_2_size_1.csv',\n",
       " 'feeder_3-8.58': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_3_feeder_3.csv',\n",
       " 'gripper_1-8.59': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_4_gripper_1.csv',\n",
       " 'max_Vel_2-8.60': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_5_max_Vel_2.csv',\n",
       " 'size_1-8.61': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_6_size_1.csv',\n",
       " 'feeder_3-8.62': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_7_feeder_3.csv',\n",
       " 'gripper_1-8.63': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_8_gripper_1.csv',\n",
       " 'max_Vel_2-8.64': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_9_max_Vel_2.csv'}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aaa24f2",
   "metadata": {},
   "source": [
    "## Run RCA trough folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A warm-up phase is considered. Each product takes roughly 26.30 seconds from the assembly entry point to the assembly end of line. There is some distance from the drop point of the items to the start and some interventions need time to become stable, thus we need to skip in total roughly 839 rows. Each 8 seconds a new product exits the assembly line."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a52c8120",
   "metadata": {},
   "source": [
    "### Algorithm #1 - Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f957d58a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'normal-1': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_14-21-20\\\\data_1_normal.csv', 'normal-2': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_20-12-23\\\\data_1_normal.csv', 'normal-3': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-01_21-53-40\\\\data_1_normal.csv', 'normal-4': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_00-47-34\\\\data_1_normal.csv', 'normal-5': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_01-23-37\\\\data_1_normal.csv', 'normal-6': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_14-32-37\\\\data_1_normal.csv', 'normal-7': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_15-17-43\\\\data_1_normal.csv', 'normal-8': 'G:\\\\My Drive\\\\Master Thesis\\\\Simulation\\\\Dataset\\\\Dataset_2024-08-06_16-12-33\\\\data_1_normal.csv'}\n"
     ]
    }
   ],
   "source": [
    "result_HT = run_HT(folder_path,files,startrow=839,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "25a8c178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.1_1</th>\n",
       "      <th>feeder_3-1.2_2</th>\n",
       "      <th>gripper_1-1.3_3</th>\n",
       "      <th>max_Vel_2-1.4_4</th>\n",
       "      <th>size_1-1.5_5</th>\n",
       "      <th>feeder_3-1.6_6</th>\n",
       "      <th>gripper_1-1.7_7</th>\n",
       "      <th>max_Vel_2-1.8_8</th>\n",
       "      <th>size_1-2.9_9</th>\n",
       "      <th>feeder_3-2.10_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.55_55</th>\n",
       "      <th>max_Vel_2-7.56_56</th>\n",
       "      <th>size_1-8.57_57</th>\n",
       "      <th>feeder_3-8.58_58</th>\n",
       "      <th>gripper_1-8.59_59</th>\n",
       "      <th>max_Vel_2-8.60_60</th>\n",
       "      <th>size_1-8.61_61</th>\n",
       "      <th>feeder_3-8.62_62</th>\n",
       "      <th>gripper_1-8.63_63</th>\n",
       "      <th>max_Vel_2-8.64_64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HT</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  size_1-1.1_1  \\\n",
       "HT  [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                      feeder_3-1.2_2  \\\n",
       "HT  [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "\n",
       "                               gripper_1-1.3_3  \\\n",
       "HT  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                               max_Vel_2-1.4_4  \\\n",
       "HT  [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                  size_1-1.5_5  \\\n",
       "HT  [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                      feeder_3-1.6_6  \\\n",
       "HT  [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "\n",
       "                               gripper_1-1.7_7  \\\n",
       "HT  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                               max_Vel_2-1.8_8  \\\n",
       "HT  [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                  size_1-2.9_9  \\\n",
       "HT  [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                    feeder_3-2.10_10  ...  \\\n",
       "HT  [cam_3_X, rob_1_maxVel, EoL_2_X]  ...   \n",
       "\n",
       "                             gripper_1-7.55_55  \\\n",
       "HT  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                             max_Vel_2-7.56_56  \\\n",
       "HT  [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                size_1-8.57_57  \\\n",
       "HT  [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                    feeder_3-8.58_58  \\\n",
       "HT  [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "\n",
       "                             gripper_1-8.59_59  \\\n",
       "HT  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                             max_Vel_2-8.60_60  \\\n",
       "HT  [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                size_1-8.61_61  \\\n",
       "HT  [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                    feeder_3-8.62_62  \\\n",
       "HT  [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "\n",
       "                             gripper_1-8.63_63  \\\n",
       "HT  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                             max_Vel_2-8.64_64  \n",
       "HT  [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "\n",
       "[1 rows x 64 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_HT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e663bf75",
   "metadata": {},
   "source": [
    "### Algorithm #2 - Epsilon Diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3dbec1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ED = run_ED_size(folder_path,files,startrow=839,size_p=2.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75a7998",
   "metadata": {},
   "source": [
    "### Algorithm #3 - Random Walk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f197b4e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW = run_RW_size(folder_path,files,startrow=839,size_p=2.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f294504a",
   "metadata": {},
   "source": [
    "### Algorithm #4: RCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "401cf78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RCD = run_RCD_size(folder_path,files,startrow=839,size_p=2.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad97be24",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095d5435",
   "metadata": {},
   "source": [
    "### Summary Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edd47ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_all = pd.DataFrame()\n",
    "result_all = pd.concat([result_HT,result_ED,result_RW,result_RCD])\n",
    "\n",
    "result_all_size_1 = result_all.filter(like='size_1')\n",
    "result_all_feeder_3 = result_all.filter(like='feeder_3')\n",
    "result_all_gripper_1 = result_all.filter(like='gripper_1')\n",
    "result_all_max_Vel_2 = result_all.filter(like='max_Vel_2')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8421b281",
   "metadata": {},
   "source": [
    "###  Intervention - Root Cause"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b8bdf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "abnormal_sets = pd.DataFrame()\n",
    "abnormal_sets['size_1'] = ['cam_1_X', 'cam_1_Y']\n",
    "abnormal_sets['feeder_3'] = ['cam_3_X', 'cam_3_Y']\n",
    "abnormal_sets['gripper_1'] = ['rob_1_supply',None]\n",
    "abnormal_sets['max_Vel_2'] = ['rob_2_maxVel',None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1303382",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_dir = 'G:\\My Drive\\Master Thesis\\Simulation\\Results'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80492867",
   "metadata": {},
   "source": [
    "### TOP 3  - Root Cause"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac209090",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top = results_top_3(result_all,abnormal_sets)\n",
    "result_data_3top.tail()\n",
    "\n",
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8155eb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top_size_1 = results_top_3(result_all_size_1,abnormal_sets)\n",
    "result_data_3top_size_1.tail()\n",
    "\n",
    "result_data_3top_size_1.to_csv(os.path.join(main_dir, 'result_data_3top_size_1.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be009313",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top_feeder_3 = results_top_3(result_all_feeder_3,abnormal_sets)\n",
    "result_data_3top_feeder_3.tail()\n",
    "\n",
    "result_data_3top_feeder_3.to_csv(os.path.join(main_dir, 'result_data_3top_feeder_3.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "14f194ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top_gripper_1 = results_top_3(result_all_gripper_1,abnormal_sets)\n",
    "result_data_3top_gripper_1.tail()\n",
    "\n",
    "result_data_3top_gripper_1.to_csv(os.path.join(main_dir, 'result_data_3top_gripper_1.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab7f78b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top_max_Vel_2 = results_top_3(result_all_max_Vel_2,abnormal_sets)\n",
    "result_data_3top_max_Vel_2.tail()\n",
    "\n",
    "result_data_3top_max_Vel_2.to_csv(os.path.join(main_dir, 'result_data_3top_max_Vel_2.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41995172",
   "metadata": {},
   "source": [
    "### TOP 1  - Root Cause"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56f535fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_1top = results_top_1(result_all,abnormal_sets)\n",
    "result_data_1top.tail()\n",
    "\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "430cc5f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_1top_size_1 = results_top_1(result_all_size_1,abnormal_sets)\n",
    "result_data_1top_size_1.tail()\n",
    "\n",
    "result_data_1top_size_1.to_csv(os.path.join(main_dir, 'result_data_1top_size_1.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b8e987b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_1top_feeder_3 = results_top_1(result_all_feeder_3,abnormal_sets)\n",
    "result_data_1top_feeder_3.tail()\n",
    "\n",
    "result_data_1top_feeder_3.to_csv(os.path.join(main_dir, 'result_data_1top_feeder_3.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5b5567e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_1top_gripper_1 = results_top_1(result_all_gripper_1,abnormal_sets)\n",
    "result_data_1top_gripper_1.tail()\n",
    "\n",
    "result_data_1top_gripper_1.to_csv(os.path.join(main_dir, 'result_data_1top_gripper_1.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43bb76c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_1top_max_Vel_2 = results_top_1(result_all_max_Vel_2,abnormal_sets)\n",
    "result_data_1top_max_Vel_2.tail()\n",
    "\n",
    "result_data_1top_max_Vel_2.to_csv(os.path.join(main_dir, 'result_data_1top_max_Vel_2.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "93cf010b",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "can only convert an array of size 1 to a Python scalar",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[37], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mresult_data_1top\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mED\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Francisco\\anaconda3\\envs\\thesis\\lib\\site-packages\\pandas\\core\\base.py:418\u001b[0m, in \u001b[0;36mIndexOpsMixin.item\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    417\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan only convert an array of size 1 to a Python scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: can only convert an array of size 1 to a Python scalar"
     ]
    }
   ],
   "source": [
    "result_data_1top['ED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e28f8116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'gripper'}>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = pd.read_csv(os.path.join(main_dir, 'result_data_1top_size_1.csv'))\n",
    "result_data_1top_gripper_1.plot.bar(y='HT', x = 'Total', title = 'size_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95fece38",
   "metadata": {},
   "source": [
    "## Variation 1 - Overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5aafce",
   "metadata": {},
   "source": [
    "### HT -Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ddc2b45a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_HT_overlap_1 = run_HT_overlap(folder_path,files,startrow=839,overlap_p=0.10,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_overlap_2 = run_HT_overlap(folder_path,files,startrow=839,overlap_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_overlap_3 = run_HT_overlap(folder_path,files,startrow=839,overlap_p=0.50,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_overlap_4 = run_HT_overlap(folder_path,files,startrow=839,overlap_p=0.75,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_overlap_5 = run_HT_overlap(folder_path,files,startrow=839,overlap_p=0.95,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2e2c405e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overlap Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_3]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_3]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_3]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_3]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_3]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               size_1-1.2_1  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                             feeder_3-1.3_2  \\\n",
       "Overlap Percent                                               \n",
       "10%                   [rob_2_maxVel, rob_1_maxVel, rob_2_3]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                            gripper_1-1.4_3  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "75%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "95%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                            max_Vel_2-1.5_4  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                               size_1-1.6_5  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                             feeder_3-1.7_6  \\\n",
       "Overlap Percent                                               \n",
       "10%                   [rob_2_maxVel, rob_1_maxVel, rob_2_3]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                            gripper_1-1.8_7  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "75%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "95%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                            max_Vel_2-1.9_8  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                               size_1-2.2_9  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                            feeder_3-2.3_10  ...  \\\n",
       "Overlap Percent                                              ...   \n",
       "10%                   [rob_2_maxVel, rob_1_maxVel, rob_2_3]  ...   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]  ...   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]  ...   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]  ...   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]  ...   \n",
       "\n",
       "                                           gripper_1-7.8_55  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "75%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "95%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                           max_Vel_2-7.9_56  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                              size_1-8.2_57  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                            feeder_3-8.3_58  \\\n",
       "Overlap Percent                                               \n",
       "10%                   [rob_2_maxVel, rob_1_maxVel, rob_2_3]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                           gripper_1-8.4_59  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "75%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "95%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                           max_Vel_2-8.5_60  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                              size_1-8.6_61  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                            feeder_3-8.7_62  \\\n",
       "Overlap Percent                                               \n",
       "10%                   [rob_2_maxVel, rob_1_maxVel, rob_2_3]   \n",
       "20%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "50%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "75%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "95%              [rob_1_maxVel, rob_2_maxVel, rob_1_vacuum]   \n",
       "\n",
       "                                           gripper_1-8.8_63  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "75%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "95%              [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                           max_Vel_2-8.9_64  \n",
       "Overlap Percent                                              \n",
       "10%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "20%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "50%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "75%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "95%              [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_overlap_HT = pd.DataFrame()\n",
    "result_overlap_HT = pd.concat([result_HT_overlap_1,result_HT_overlap_2,result_HT_overlap_3,result_HT_overlap_4,result_HT_overlap_5], ignore_index=True)\n",
    "result_overlap_HT['Overlap Percent'] = pd.DataFrame({'HT':['10%','20%','50%','75%','95%']})\n",
    "result_overlap_HT.set_index('Overlap Percent', inplace=True)\n",
    "result_overlap_HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "90775c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  10%  20%  50%  75%  95%\n",
       "size_1-8.6_61     0.0  0.0  0.0  0.0  0.0\n",
       "feeder_3-8.7_62   0.0  0.0  0.0  0.0  0.0\n",
       "gripper_1-8.8_63  1.0  1.0  1.0  1.0  1.0\n",
       "max_Vel_2-8.9_64  1.0  1.0  1.0  1.0  1.0\n",
       "Total             0.5  0.5  0.5  0.5  0.5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_overlap_HT,abnormal_sets)\n",
    "result_data_3top.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7b5980ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  10%  20%  50%  75%  95%\n",
       "size_1-8.6_61     0.0  0.0  0.0  0.0  0.0\n",
       "feeder_3-8.7_62   0.0  0.0  0.0  0.0  0.0\n",
       "gripper_1-8.8_63  1.0  1.0  1.0  1.0  1.0\n",
       "max_Vel_2-8.9_64  1.0  1.0  1.0  1.0  1.0\n",
       "Total             0.5  0.5  0.5  0.5  0.5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_overlap_HT,abnormal_sets)\n",
    "result_data_1top.tail()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ba073273",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_1_HT.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_1_HT.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8feaf11",
   "metadata": {},
   "source": [
    "### ED - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e8c5bd88",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ED_overlap_1 = run_ED_overlap(folder_path,files,startrow=839,overlap_p=0.10,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_overlap_2 = run_ED_overlap(folder_path,files,startrow=839,overlap_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_overlap_3 = run_ED_overlap(folder_path,files,startrow=839,overlap_p=0.50,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_overlap_4 = run_ED_overlap(folder_path,files,startrow=839,overlap_p=0.75,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_overlap_5 = run_ED_overlap(folder_path,files,startrow=839,overlap_p=0.95,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ad854d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overlap Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[cam_2_Y, cam_3_X, EoL_1_Y]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, con_2, EoL_3_X]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_1_X, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_1_Y, EoL_1_X, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, con_3, EoL_3_X]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_1_X, EoL_2_Y, rob_1_3]</td>\n",
       "      <td>[cam_3_X, EoL_1_Y, EoL_3_X]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, con_1]</td>\n",
       "      <td>[EoL_1_X, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_1_X, EoL_2_Y, rob_1_3]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_X, con_2, cam_3_X]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_1_X, EoL_2_Y, rob_1_3]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_1_X, EoL_2_X, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_1_X, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, con_3]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_1_X, EoL_2_Y, rob_1_3]</td>\n",
       "      <td>[EoL_2_X, con_2, cam_3_X]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, rob_1_3, rob_1_2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[con_3, EoL_2_Y, con_1]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, con_1]</td>\n",
       "      <td>[con_2, EoL_2_X, con_1]</td>\n",
       "      <td>[EoL_2_Y, con_3, rob_1_3]</td>\n",
       "      <td>[EoL_2_Y, con_2, EoL_2_X]</td>\n",
       "      <td>[cam_3_X, con_2, EoL_2_X]</td>\n",
       "      <td>[EoL_2_X, con_3, con_1]</td>\n",
       "      <td>[EoL_2_Y, con_1, con_3]</td>\n",
       "      <td>[EoL_2_Y, con_2, con_1]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, con_3]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_2_X, con_3, con_2]</td>\n",
       "      <td>[EoL_2_Y, con_2, con_3]</td>\n",
       "      <td>[EoL_2_Y, con_3, con_1]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, con_3]</td>\n",
       "      <td>[EoL_2_X, con_3, con_1]</td>\n",
       "      <td>[EoL_4_Y, con_2, con_1]</td>\n",
       "      <td>[EoL_2_Y, con_1, con_3]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, con_2]</td>\n",
       "      <td>[EoL_2_X, con_1, con_2]</td>\n",
       "      <td>[EoL_2_Y, con_2, con_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_6_X, cam_3_X, EoL_5_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_X]</td>\n",
       "      <td>[cam_3_Y, EoL_6_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[cam_3_Y, EoL_5_X, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, cam_3_Y]</td>\n",
       "      <td>[cam_3_X, cam_3_Y, EoL_6_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>[cam_3_X, rob_2_vacuum, EoL_1_X]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, EoL_3_X]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, rob_2_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>[cam_3_X, rob_2_vacuum, EoL_1_X]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, EoL_3_X]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_X, rob_2_vacuum, EoL_1_X]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, EoL_3_X]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>[cam_3_X, rob_2_vacuum, EoL_1_X]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_vacuum]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, rob_2_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>[cam_3_X, rob_2_vacuum, EoL_1_X]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, EoL_3_X]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     size_1-1.2_1  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                       [con_3, EoL_2_Y, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, EoL_2_X, rob_2_vacuum]   \n",
       "\n",
       "                                   feeder_3-1.3_2  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [cam_2_Y, cam_3_X, EoL_1_Y]   \n",
       "20%                     [EoL_2_X, cam_2_Y, con_1]   \n",
       "50%                     [cam_3_X, EoL_2_X, con_1]   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_3_Y]   \n",
       "95%              [rob_2_vacuum, EoL_1_X, EoL_1_Y]   \n",
       "\n",
       "                                  gripper_1-1.4_3  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [EoL_1_X, rob_1_3, rob_1_2]   \n",
       "50%                       [con_2, EoL_2_X, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, rob_2_vacuum, EoL_1_X]   \n",
       "\n",
       "                             max_Vel_2-1.5_4  \\\n",
       "Overlap Percent                                \n",
       "10%              [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%              [EoL_1_X, EoL_2_Y, rob_1_3]   \n",
       "50%                [EoL_2_Y, con_3, rob_1_3]   \n",
       "75%              [EoL_6_X, cam_3_X, EoL_5_X]   \n",
       "95%              [EoL_2_X, cam_3_X, EoL_3_X]   \n",
       "\n",
       "                                     size_1-1.6_5  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "20%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                     [EoL_2_Y, con_2, EoL_2_X]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, EoL_6_X]   \n",
       "95%              [EoL_2_X, cam_3_X, rob_2_vacuum]   \n",
       "\n",
       "                                   feeder_3-1.7_6  \\\n",
       "Overlap Percent                                     \n",
       "10%                     [cam_3_X, con_2, EoL_3_X]   \n",
       "20%                     [EoL_2_X, con_2, cam_3_X]   \n",
       "50%                     [cam_3_X, con_2, EoL_2_X]   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_3_Y]   \n",
       "95%              [rob_2_vacuum, EoL_1_X, EoL_1_Y]   \n",
       "\n",
       "                                  gripper_1-1.8_7  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "50%                       [EoL_2_X, con_3, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_X]   \n",
       "95%              [cam_3_X, rob_2_vacuum, EoL_1_X]   \n",
       "\n",
       "                             max_Vel_2-1.9_8  \\\n",
       "Overlap Percent                                \n",
       "10%              [EoL_1_X, rob_1_3, rob_1_2]   \n",
       "20%              [EoL_1_X, EoL_2_Y, rob_1_3]   \n",
       "50%                  [EoL_2_Y, con_1, con_3]   \n",
       "75%              [cam_3_Y, EoL_6_X, EoL_2_Y]   \n",
       "95%              [EoL_2_X, cam_3_X, EoL_3_X]   \n",
       "\n",
       "                                     size_1-2.2_9  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "20%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                       [EoL_2_Y, con_2, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, EoL_2_X, rob_2_vacuum]   \n",
       "\n",
       "                                  feeder_3-2.3_10  ...  \\\n",
       "Overlap Percent                                    ...   \n",
       "10%                   [EoL_1_Y, EoL_1_X, cam_3_X]  ...   \n",
       "20%                   [EoL_1_X, EoL_2_X, cam_3_X]  ...   \n",
       "50%                     [cam_3_X, EoL_2_X, con_3]  ...   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_3_Y]  ...   \n",
       "95%              [rob_2_vacuum, EoL_1_X, EoL_1_Y]  ...   \n",
       "\n",
       "                                 gripper_1-7.8_55  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [EoL_1_X, rob_1_3, rob_1_2]   \n",
       "50%                       [EoL_2_X, con_3, con_2]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, rob_2_vacuum, EoL_1_X]   \n",
       "\n",
       "                            max_Vel_2-7.9_56  \\\n",
       "Overlap Percent                                \n",
       "10%              [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "20%              [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                  [EoL_2_Y, con_2, con_3]   \n",
       "75%              [cam_3_Y, EoL_5_X, EoL_6_X]   \n",
       "95%              [EoL_2_X, cam_3_X, EoL_3_X]   \n",
       "\n",
       "                                    size_1-8.2_57  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "20%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                       [EoL_2_Y, con_3, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, EoL_2_X, rob_2_vacuum]   \n",
       "\n",
       "                                  feeder_3-8.3_58  \\\n",
       "Overlap Percent                                     \n",
       "10%                     [cam_3_X, con_3, EoL_3_X]   \n",
       "20%                     [EoL_2_X, cam_3_X, con_3]   \n",
       "50%                     [cam_3_X, EoL_2_X, con_3]   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_3_Y]   \n",
       "95%              [rob_2_vacuum, EoL_1_X, EoL_1_Y]   \n",
       "\n",
       "                                 gripper_1-8.4_59  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "50%                       [EoL_2_X, con_3, con_1]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]   \n",
       "95%              [cam_3_X, rob_2_vacuum, EoL_1_X]   \n",
       "\n",
       "                                 max_Vel_2-8.5_60  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]   \n",
       "20%                   [EoL_2_Y, rob_1_3, rob_1_2]   \n",
       "50%                       [EoL_4_Y, con_2, con_1]   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_6_X]   \n",
       "95%              [cam_3_X, EoL_2_X, rob_2_vacuum]   \n",
       "\n",
       "                                    size_1-8.6_61  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_1_X, EoL_2_Y, rob_1_3]   \n",
       "20%                   [EoL_1_X, EoL_2_Y, rob_1_3]   \n",
       "50%                       [EoL_2_Y, con_1, con_3]   \n",
       "75%                   [EoL_4_Y, EoL_3_Y, EoL_6_X]   \n",
       "95%              [EoL_2_X, cam_3_X, rob_2_vacuum]   \n",
       "\n",
       "                                  feeder_3-8.7_62  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [cam_3_X, EoL_1_Y, EoL_3_X]   \n",
       "20%                     [EoL_2_X, con_2, cam_3_X]   \n",
       "50%                     [cam_3_X, EoL_2_X, con_2]   \n",
       "75%                   [EoL_4_Y, cam_3_X, EoL_3_Y]   \n",
       "95%              [rob_2_vacuum, EoL_1_X, EoL_1_Y]   \n",
       "\n",
       "                                 gripper_1-8.8_63             max_Vel_2-8.9_64  \n",
       "Overlap Percent                                                                 \n",
       "10%                   [rob_1_3, rob_1_2, rob_1_4]  [EoL_2_Y, rob_1_3, rob_1_2]  \n",
       "20%                   [rob_1_3, rob_1_2, rob_1_4]  [EoL_2_Y, rob_1_3, rob_1_2]  \n",
       "50%                       [EoL_2_X, con_1, con_2]      [EoL_2_Y, con_2, con_1]  \n",
       "75%                   [EoL_4_Y, EoL_3_Y, cam_3_Y]  [cam_3_X, cam_3_Y, EoL_6_X]  \n",
       "95%              [cam_3_X, rob_2_vacuum, EoL_1_X]  [cam_3_X, EoL_2_X, EoL_3_X]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_overlap_ED = pd.DataFrame()\n",
    "result_overlap_ED = pd.concat([result_ED_overlap_1,result_ED_overlap_2,result_ED_overlap_3,result_ED_overlap_4,result_ED_overlap_5], ignore_index=True)\n",
    "result_overlap_ED['Overlap Percent'] = pd.DataFrame({'ED':['10%','20%','50%','75%','95%']})\n",
    "result_overlap_ED.set_index('Overlap Percent', inplace=True)\n",
    "result_overlap_ED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "55b26d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   10%       20%   50%   75%  95%\n",
       "size_1-8.6_61     0.00  0.000000  0.00  0.00  0.0\n",
       "feeder_3-8.7_62   1.00  1.000000  1.00  1.00  0.0\n",
       "gripper_1-8.8_63  0.00  0.000000  0.00  0.00  0.0\n",
       "max_Vel_2-8.9_64  0.00  0.000000  0.00  0.00  0.0\n",
       "Total             0.25  0.171875  0.25  0.25  0.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_overlap_ED,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7638aab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.109375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       10%  20%   50%  75%  95%\n",
       "size_1-8.6_61     0.000000  0.0  0.00  0.0  0.0\n",
       "feeder_3-8.7_62   1.000000  0.0  1.00  0.0  0.0\n",
       "gripper_1-8.8_63  0.000000  0.0  0.00  0.0  0.0\n",
       "max_Vel_2-8.9_64  0.000000  0.0  0.00  0.0  0.0\n",
       "Total             0.109375  0.0  0.25  0.0  0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_overlap_ED,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "45ddd249",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_1_ED.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_1_ED.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a3c856",
   "metadata": {},
   "source": [
    "### RW - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "43587c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW_overlap_1 = run_RW_overlap(folder_path,files,startrow=839,overlap_p=0.10,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_overlap_2 = run_RW_overlap(folder_path,files,startrow=839,overlap_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_overlap_3 = run_RW_overlap(folder_path,files,startrow=839,overlap_p=0.50,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_overlap_4 = run_RW_overlap(folder_path,files,startrow=839,overlap_p=0.75,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_overlap_5 = run_RW_overlap(folder_path,files,startrow=839,overlap_p=0.95,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "35564984",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overlap Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[cam_1_Y, cam_1_X, con_2]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, con_2]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, con_2]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_1_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>[rob_2_supply, cam_1_X, con_2]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[cam_1_Y, con_2, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, con_2]</td>\n",
       "      <td>[cam_1_Y, rob_2_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, con_2]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, cam_3_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, con_2, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[con_2, cam_3_Y, rob_2_supply]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, con_2, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, con_2, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, con_2, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        size_1-1.2_1  \\\n",
       "Overlap Percent                                        \n",
       "10%                 [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "20%                        [cam_1_Y, cam_1_X, con_2]   \n",
       "50%                 [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "75%                   [rob_2_supply, cam_1_X, con_2]   \n",
       "95%              [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                        feeder_3-1.3_2  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_2_supply, rob_1_supply, cam_3_Y]   \n",
       "20%              [rob_2_supply, cam_1_Y, rob_1_supply]   \n",
       "50%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "75%              [rob_1_supply, cam_1_Y, rob_2_maxVel]   \n",
       "95%                   [rob_2_supply, cam_3_Y, cam_3_X]   \n",
       "\n",
       "                                       gripper_1-1.4_3  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "75%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "95%                [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                            max_Vel_2-1.5_4  \\\n",
       "Overlap Percent                                               \n",
       "10%              [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "20%              [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "50%                     [rob_2_supply, rob_2_maxVel, con_2]   \n",
       "75%                   [rob_2_maxVel, cam_1_Y, rob_1_supply]   \n",
       "95%                     [rob_2_maxVel, con_2, rob_1_supply]   \n",
       "\n",
       "                                        size_1-1.6_5  \\\n",
       "Overlap Percent                                        \n",
       "10%                 [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "20%                 [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "50%                 [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "75%                        [cam_1_Y, con_2, cam_1_X]   \n",
       "95%              [rob_2_supply, con_2, rob_1_supply]   \n",
       "\n",
       "                                        feeder_3-1.7_6  \\\n",
       "Overlap Percent                                          \n",
       "10%                [rob_2_supply, con_2, rob_1_supply]   \n",
       "20%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "50%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "75%                   [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "95%                [rob_1_supply, con_2, rob_2_supply]   \n",
       "\n",
       "                                       gripper_1-1.8_7  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%                   [rob_1_supply, cam_1_Y, cam_1_X]   \n",
       "75%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "95%                [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                       max_Vel_2-1.9_8  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "20%              [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "50%                   [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "75%              [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "95%              [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "\n",
       "                                          size_1-2.2_9  \\\n",
       "Overlap Percent                                          \n",
       "10%                   [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "20%                   [cam_1_Y, rob_2_supply, cam_1_X]   \n",
       "50%                   [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "75%                     [rob_2_supply, cam_1_Y, con_2]   \n",
       "95%              [rob_2_supply, cam_1_Y, rob_1_supply]   \n",
       "\n",
       "                                       feeder_3-2.3_10  ...  \\\n",
       "Overlap Percent                                         ...   \n",
       "10%                   [rob_1_supply, cam_1_Y, cam_3_X]  ...   \n",
       "20%              [rob_2_supply, rob_1_supply, cam_1_Y]  ...   \n",
       "50%              [rob_2_supply, cam_1_Y, rob_1_supply]  ...   \n",
       "75%                     [cam_1_Y, rob_2_supply, con_2]  ...   \n",
       "95%                     [con_2, cam_3_Y, rob_2_supply]  ...   \n",
       "\n",
       "                                      gripper_1-7.8_55  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "75%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "95%                [rob_1_supply, con_2, rob_2_supply]   \n",
       "\n",
       "                                           max_Vel_2-7.9_56  \\\n",
       "Overlap Percent                                               \n",
       "10%                   [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "20%                   [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "50%                   [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "75%                     [rob_2_maxVel, rob_2_supply, con_2]   \n",
       "95%              [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "\n",
       "                                       size_1-8.2_57  \\\n",
       "Overlap Percent                                        \n",
       "10%                 [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "20%                 [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "50%                 [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "75%                 [cam_1_Y, cam_1_X, rob_1_supply]   \n",
       "95%              [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                       feeder_3-8.3_58  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "50%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "75%                   [rob_1_supply, cam_1_Y, cam_3_Y]   \n",
       "95%                [rob_2_supply, con_2, rob_1_supply]   \n",
       "\n",
       "                                      gripper_1-8.4_59  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "75%              [rob_1_supply, cam_1_Y, rob_2_maxVel]   \n",
       "95%              [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "\n",
       "                                           max_Vel_2-8.5_60  \\\n",
       "Overlap Percent                                               \n",
       "10%                     [rob_2_maxVel, con_2, rob_1_supply]   \n",
       "20%                     [rob_2_maxVel, rob_2_supply, con_2]   \n",
       "50%                   [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "75%              [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "95%                   [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "\n",
       "                                    size_1-8.6_61  \\\n",
       "Overlap Percent                                     \n",
       "10%              [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "20%              [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "50%              [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "75%                [cam_1_X, con_2, rob_1_supply]   \n",
       "95%                [rob_2_supply, con_2, cam_1_Y]   \n",
       "\n",
       "                                       feeder_3-8.7_62  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "20%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "50%              [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "75%                [rob_2_supply, rob_1_supply, con_2]   \n",
       "95%                [rob_2_supply, con_2, rob_1_supply]   \n",
       "\n",
       "                                      gripper_1-8.8_63  \\\n",
       "Overlap Percent                                          \n",
       "10%              [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%              [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%                [rob_1_supply, rob_2_supply, con_2]   \n",
       "75%                [rob_1_supply, rob_2_supply, con_2]   \n",
       "95%                     [rob_1_supply, con_2, cam_1_Y]   \n",
       "\n",
       "                                           max_Vel_2-8.9_64  \n",
       "Overlap Percent                                              \n",
       "10%                   [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "20%              [rob_2_maxVel, rob_2_supply, rob_1_supply]  \n",
       "50%                   [rob_2_maxVel, cam_1_Y, rob_1_supply]  \n",
       "75%                   [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "95%                   [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_overlap_RW = pd.DataFrame()\n",
    "result_overlap_RW = pd.concat([result_RW_overlap_1,result_RW_overlap_2,result_RW_overlap_3,result_RW_overlap_4,result_RW_overlap_5], ignore_index=True)\n",
    "result_overlap_RW['Overlap Percent'] = pd.DataFrame({'RW':['10%','20%','50%','75%','95%']})\n",
    "result_overlap_RW.set_index('Overlap Percent', inplace=True)\n",
    "result_overlap_RW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e9b28b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.8125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     10%   20%   50%   75%     95%\n",
       "size_1-8.6_61     1.0000  1.00  1.00  1.00  1.0000\n",
       "feeder_3-8.7_62   1.0000  0.00  0.00  0.00  0.0000\n",
       "gripper_1-8.8_63  1.0000  1.00  1.00  1.00  1.0000\n",
       "max_Vel_2-8.9_64  1.0000  1.00  1.00  1.00  1.0000\n",
       "Total             0.8125  0.75  0.75  0.75  0.6875"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_overlap_RW,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fafe1a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.53125</td>\n",
       "      <td>0.546875</td>\n",
       "      <td>0.46875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       10%       20%      50%       75%      95%\n",
       "size_1-8.6_61     0.000000  1.000000  1.00000  1.000000  0.00000\n",
       "feeder_3-8.7_62   0.000000  0.000000  0.00000  0.000000  0.00000\n",
       "gripper_1-8.8_63  1.000000  1.000000  1.00000  1.000000  1.00000\n",
       "max_Vel_2-8.9_64  1.000000  1.000000  1.00000  1.000000  1.00000\n",
       "Total             0.484375  0.546875  0.53125  0.546875  0.46875"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_overlap_RW,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "858fd970",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_1_RW.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_1_RW.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8f6027",
   "metadata": {},
   "source": [
    "### RCD - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ce8198d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RCD_overlap_1 = run_RCD_overlap(folder_path,files,startrow=839,overlap_p=0.10,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_overlap_2 = run_RCD_overlap(folder_path,files,startrow=839,overlap_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_overlap_3 = run_RCD_overlap(folder_path,files,startrow=839,overlap_p=0.50,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_overlap_4 = run_RCD_overlap(folder_path,files,startrow=839,overlap_p=0.75,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_overlap_5 = run_RCD_overlap(folder_path,files,startrow=839,overlap_p=0.95,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "1288a29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overlap Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>[EoL_3_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_2_Y, rob_1_supply, cam_2_Y]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, cam_2_Y]</td>\n",
       "      <td>[rob_1_supply, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_3_Y, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_3_X, rob_1_supply, cam_2_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_3_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_2_X, cam_2_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[EoL_3_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_1_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_6_Y, cam_3_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, cam_2_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_3_X, cam_2_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_6_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[cam_3_Y, EoL_3_X, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_6_X, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[EoL_4_X, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_1_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_1_Y, cam_3_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, EoL_2_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_2_X, EoL_3_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_X]</td>\n",
       "      <td>[EoL_6_X, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[cam_1_X, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[cam_3_X, EoL_2_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_X]</td>\n",
       "      <td>[EoL_6_X, EoL_2_X, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, EoL_2_X, cam_2_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_3_X, cam_2_Y]</td>\n",
       "      <td>[rob_2_maxVel, EoL_2_X, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>[EoL_3_X, cam_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_X, cam_3_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_1_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_3_X, cam_2_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[rob_2_maxVel, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_Y, cam_3_Y, EoL_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_3_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_2_X, cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_2_Y, EoL_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_2_Y, EoL_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>[cam_3_Y, EoL_3_Y]</td>\n",
       "      <td>[cam_3_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_3_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X]</td>\n",
       "      <td>[EoL_5_X, cam_2_Y]</td>\n",
       "      <td>[cam_3_Y, cam_3_X]</td>\n",
       "      <td>[cam_3_Y, rob_1_supply]</td>\n",
       "      <td>[EoL_6_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_3_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_2_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_3_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, EoL_6_X]</td>\n",
       "      <td>[EoL_5_X, cam_3_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_3_X]</td>\n",
       "      <td>[rob_1_supply, EoL_3_X, cam_3_X]</td>\n",
       "      <td>[EoL_6_X, cam_3_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_3_Y]</td>\n",
       "      <td>[cam_3_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_X, EoL_2_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_X, cam_3_X]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                size_1-1.2_1               feeder_3-1.3_2  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [EoL_3_X, cam_2_Y, EoL_1_Y]  [EoL_2_Y, EoL_1_Y, cam_2_Y]   \n",
       "20%              [EoL_3_X, cam_2_Y, EoL_1_Y]  [EoL_3_X, EoL_1_Y, cam_2_Y]   \n",
       "50%              [cam_1_Y, EoL_2_X, cam_2_Y]  [EoL_2_Y, EoL_1_Y, cam_1_Y]   \n",
       "75%              [EoL_3_X, cam_3_Y, EoL_4_X]  [EoL_2_X, cam_2_Y, EoL_2_Y]   \n",
       "95%                       [cam_3_Y, EoL_3_Y]                    [cam_3_Y]   \n",
       "\n",
       "                                  gripper_1-1.4_3  \\\n",
       "Overlap Percent                                     \n",
       "10%              [EoL_2_Y, rob_1_supply, cam_2_Y]   \n",
       "20%                   [EoL_3_X, cam_2_Y, EoL_1_Y]   \n",
       "50%                   [EoL_2_Y, cam_1_Y, cam_3_X]   \n",
       "75%                   [EoL_2_Y, cam_2_Y, EoL_1_Y]   \n",
       "95%                            [EoL_5_X, EoL_3_X]   \n",
       "\n",
       "                             max_Vel_2-1.5_4                 size_1-1.6_5  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [cam_2_Y, EoL_1_Y, EoL_2_Y]  [EoL_3_X, cam_2_Y, EoL_1_Y]   \n",
       "20%              [cam_2_Y, EoL_1_Y, EoL_2_Y]  [EoL_3_X, EoL_1_Y, cam_3_Y]   \n",
       "50%              [EoL_6_Y, EoL_2_X, cam_1_X]  [cam_1_Y, EoL_2_X, cam_2_Y]   \n",
       "75%                       [cam_2_Y, EoL_1_Y]  [EoL_3_X, cam_3_Y, cam_1_Y]   \n",
       "95%                  [rob_2_maxVel, cam_3_X]           [EoL_5_X, cam_2_Y]   \n",
       "\n",
       "                              feeder_3-1.7_6  \\\n",
       "Overlap Percent                                \n",
       "10%              [EoL_3_X, cam_3_Y, cam_2_Y]   \n",
       "20%              [EoL_3_X, cam_3_Y, EoL_1_Y]   \n",
       "50%              [EoL_2_Y, EoL_1_Y, cam_1_Y]   \n",
       "75%              [EoL_2_X, cam_2_Y, EoL_2_Y]   \n",
       "95%                       [cam_3_Y, cam_3_X]   \n",
       "\n",
       "                                  gripper_1-1.8_7  \\\n",
       "Overlap Percent                                     \n",
       "10%              [rob_1_supply, cam_2_Y, cam_3_X]   \n",
       "20%                   [EoL_2_Y, EoL_1_Y, cam_3_Y]   \n",
       "50%                   [EoL_2_Y, EoL_1_Y, cam_1_Y]   \n",
       "75%                   [EoL_2_X, cam_3_Y, cam_2_Y]   \n",
       "95%                       [cam_3_Y, rob_1_supply]   \n",
       "\n",
       "                                  max_Vel_2-1.9_8  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_6_Y, EoL_2_X, cam_2_Y]   \n",
       "20%                   [EoL_6_Y, cam_3_Y, EoL_2_Y]   \n",
       "50%              [rob_2_maxVel, EoL_2_X, cam_1_Y]   \n",
       "75%                   [EoL_6_Y, EoL_1_Y, cam_3_Y]   \n",
       "95%                       [EoL_6_X, rob_2_maxVel]   \n",
       "\n",
       "                                size_1-2.2_9              feeder_3-2.3_10  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [EoL_3_Y, cam_2_Y, EoL_2_Y]  [EoL_2_Y, cam_2_Y, EoL_1_Y]   \n",
       "20%              [EoL_4_Y, cam_2_Y, EoL_1_Y]  [EoL_3_X, cam_3_Y, cam_2_Y]   \n",
       "50%              [cam_1_Y, EoL_2_X, EoL_3_X]  [EoL_2_Y, EoL_1_Y, cam_1_Y]   \n",
       "75%              [EoL_3_X, cam_2_Y, cam_3_Y]  [EoL_2_X, cam_2_Y, EoL_2_Y]   \n",
       "95%                       [EoL_3_Y, EoL_3_X]                    [EoL_2_X]   \n",
       "\n",
       "                 ...                  gripper_1-7.8_55  \\\n",
       "Overlap Percent  ...                                     \n",
       "10%              ...  [EoL_3_X, rob_1_supply, cam_2_Y]   \n",
       "20%              ...       [EoL_3_X, cam_2_Y, cam_3_Y]   \n",
       "50%              ...       [EoL_2_Y, EoL_1_Y, cam_1_X]   \n",
       "75%              ...       [EoL_2_X, cam_2_Y, EoL_2_Y]   \n",
       "95%              ...                         [EoL_3_X]   \n",
       "\n",
       "                                 max_Vel_2-7.9_56  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_6_Y, EoL_2_X, cam_2_Y]   \n",
       "20%                   [EoL_6_X, cam_2_Y, EoL_1_Y]   \n",
       "50%                   [EoL_6_X, EoL_2_X, cam_2_Y]   \n",
       "75%              [rob_2_maxVel, EoL_1_Y, cam_2_Y]   \n",
       "95%              [rob_2_maxVel, cam_3_X, EoL_6_X]   \n",
       "\n",
       "                               size_1-8.2_57              feeder_3-8.3_58  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [EoL_3_X, cam_3_Y, EoL_1_Y]  [EoL_3_X, cam_1_Y, cam_1_X]   \n",
       "20%              [EoL_4_Y, cam_2_Y, EoL_1_Y]  [cam_3_Y, EoL_3_X, EoL_1_Y]   \n",
       "50%              [cam_1_X, EoL_2_X, cam_2_Y]  [cam_3_X, EoL_2_Y, cam_2_Y]   \n",
       "75%              [EoL_3_X, cam_3_Y, EoL_4_X]  [EoL_2_Y, EoL_1_Y, cam_2_Y]   \n",
       "95%              [EoL_5_X, cam_3_X, EoL_6_Y]           [EoL_2_X, EoL_3_X]   \n",
       "\n",
       "                                 gripper_1-8.4_59  \\\n",
       "Overlap Percent                                     \n",
       "10%                   [EoL_3_Y, EoL_1_Y, cam_2_Y]   \n",
       "20%                   [EoL_3_X, EoL_1_Y, cam_2_Y]   \n",
       "50%                   [EoL_2_Y, EoL_1_Y, cam_1_X]   \n",
       "75%                   [EoL_2_X, cam_2_Y, EoL_2_Y]   \n",
       "95%              [rob_1_supply, EoL_3_X, cam_3_X]   \n",
       "\n",
       "                            max_Vel_2-8.5_60                size_1-8.6_61  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [EoL_6_Y, EoL_2_X, cam_2_Y]  [EoL_3_Y, cam_2_Y, EoL_1_Y]   \n",
       "20%              [EoL_6_X, cam_2_Y, cam_3_X]  [EoL_4_X, EoL_2_X, cam_2_Y]   \n",
       "50%              [EoL_6_X, EoL_2_X, cam_1_X]  [cam_1_Y, EoL_2_X, cam_2_Y]   \n",
       "75%              [EoL_6_Y, cam_3_Y, EoL_1_Y]  [cam_1_Y, cam_3_Y, EoL_1_Y]   \n",
       "95%              [EoL_6_X, cam_3_X, EoL_6_Y]           [EoL_3_X, EoL_3_Y]   \n",
       "\n",
       "                             feeder_3-8.7_62             gripper_1-8.8_63  \\\n",
       "Overlap Percent                                                             \n",
       "10%              [EoL_2_Y, cam_2_Y, EoL_1_Y]  [EoL_2_Y, cam_2_Y, EoL_1_Y]   \n",
       "20%              [EoL_3_X, cam_3_Y, EoL_1_Y]  [EoL_3_X, cam_2_Y, EoL_1_Y]   \n",
       "50%              [EoL_2_Y, EoL_1_Y, cam_1_Y]  [EoL_2_Y, cam_3_X, cam_2_Y]   \n",
       "75%              [EoL_2_X, cam_2_Y, EoL_2_Y]  [EoL_2_X, EoL_2_Y, EoL_1_Y]   \n",
       "95%                                [cam_3_Y]  [EoL_3_X, cam_3_X, EoL_2_X]   \n",
       "\n",
       "                                 max_Vel_2-8.9_64  \n",
       "Overlap Percent                                    \n",
       "10%                   [EoL_3_Y, EoL_2_X, cam_2_Y]  \n",
       "20%                   [EoL_3_Y, cam_2_Y, EoL_1_Y]  \n",
       "50%              [rob_2_maxVel, EoL_2_X, cam_1_Y]  \n",
       "75%              [rob_2_maxVel, cam_2_Y, EoL_1_Y]  \n",
       "95%              [rob_2_maxVel, EoL_6_X, cam_3_X]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_overlap_RCD = pd.DataFrame()\n",
    "result_overlap_RCD = pd.concat([result_RCD_overlap_1,result_RCD_overlap_2,result_RCD_overlap_3,result_RCD_overlap_4,result_RCD_overlap_5], ignore_index=True)\n",
    "result_overlap_RCD['Overlap Percent'] = pd.DataFrame({'RCD':['10%','20%','50%','75%','95%']})\n",
    "result_overlap_RCD.set_index('Overlap Percent', inplace=True)\n",
    "result_overlap_RCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "09b819c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.28125</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>0.515625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      10%       20%       50%       75%       95%\n",
       "size_1-8.6_61     0.00000  0.000000  1.000000  1.000000  0.000000\n",
       "feeder_3-8.7_62   0.00000  1.000000  0.000000  0.000000  1.000000\n",
       "gripper_1-8.8_63  0.00000  0.000000  0.000000  0.000000  0.000000\n",
       "max_Vel_2-8.9_64  0.00000  0.000000  1.000000  1.000000  1.000000\n",
       "Total             0.28125  0.203125  0.390625  0.140625  0.515625"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_overlap_RCD,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "54b1b2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10%</th>\n",
       "      <th>20%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>95%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>0.296875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      10%      20%       50%       75%       95%\n",
       "size_1-8.6_61     0.00000  0.00000  1.000000  1.000000  0.000000\n",
       "feeder_3-8.7_62   0.00000  0.00000  0.000000  0.000000  1.000000\n",
       "gripper_1-8.8_63  0.00000  0.00000  0.000000  0.000000  0.000000\n",
       "max_Vel_2-8.9_64  0.00000  0.00000  1.000000  1.000000  1.000000\n",
       "Total             0.15625  0.09375  0.390625  0.078125  0.296875"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_overlap_RCD,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cd62aa41",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_1_RCD.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_1_RCD.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e93f9147",
   "metadata": {},
   "source": [
    "## Variation 2 - Normal Size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54820a4a",
   "metadata": {},
   "source": [
    "The ED-algorithm requires the same length for the normal and abnormal data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c237377",
   "metadata": {},
   "source": [
    "### HT - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c0172d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_HT_normal_size_1 = run_HT_normal_size(folder_path,files,startrow=839,normal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_normal_size_2 = run_HT_normal_size(folder_path,files,startrow=839,normal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_normal_size_3 = run_HT_normal_size(folder_path,files,startrow=839,normal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_normal_size_4 = run_HT_normal_size(folder_path,files,startrow=839,normal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_normal_size_5 = run_HT_normal_size(folder_path,files,startrow=839,normal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4f6ae6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[con_3, con_2, rob_1_maxVel]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_2]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_2]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_2, rob_1_1]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_1_3, rob_1_1, rob_1_2]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[cam_3_X, rob_1_maxVel, EoL_2_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_2_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, score, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, score]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, score, rob_1_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, score, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, score, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         size_1-1.2_1  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "150%                     [con_3, con_2, rob_1_maxVel]   \n",
       "100%                      [rob_1_2, rob_1_1, rob_1_3]   \n",
       "50%                       [rob_1_3, rob_1_1, rob_1_2]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_maxVel]   \n",
       "\n",
       "                                            feeder_3-1.3_2  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                 [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                      [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                       [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "20%                    [rob_2_maxVel, score, rob_1_maxVel]   \n",
       "\n",
       "                                                gripper_1-1.4_3  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%                       [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "\n",
       "                                                max_Vel_2-1.5_4  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                        [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%                      [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                       [rob_2_maxVel, rob_1_maxVel, rob_2_1]   \n",
       "20%                       [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "\n",
       "                                         size_1-1.6_5  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                     [rob_1_maxVel, con_3, con_2]   \n",
       "100%                 [rob_1_maxVel, rob_1_1, rob_1_2]   \n",
       "50%                  [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_maxVel]   \n",
       "\n",
       "                                            feeder_3-1.7_6  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                 [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                      [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                       [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "20%                         [rob_2_maxVel, cam_1_X, score]   \n",
       "\n",
       "                                                gripper_1-1.8_7  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                                max_Vel_2-1.9_8  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                        [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%                      [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                       [rob_2_maxVel, rob_1_maxVel, rob_2_1]   \n",
       "20%                       [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "\n",
       "                                         size_1-2.2_9  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                     [rob_1_maxVel, con_3, con_2]   \n",
       "100%                 [rob_1_maxVel, rob_1_1, rob_1_2]   \n",
       "50%                  [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "20%                       [cam_1_X, cam_1_Y, rob_1_3]   \n",
       "\n",
       "                                           feeder_3-2.3_10  ...  \\\n",
       "Normal Size Percent                                         ...   \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "150%                 [cam_3_X, rob_2_maxVel, rob_1_maxVel]  ...   \n",
       "100%                      [rob_1_maxVel, rob_1_2, rob_1_1]  ...   \n",
       "50%                       [cam_3_X, rob_1_maxVel, EoL_2_X]  ...   \n",
       "20%                    [rob_2_maxVel, score, rob_1_maxVel]  ...   \n",
       "\n",
       "                                               gripper_1-7.8_55  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                               max_Vel_2-7.9_56  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                        [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%                      [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                       [rob_2_maxVel, rob_1_maxVel, rob_2_1]   \n",
       "20%                       [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "\n",
       "                                        size_1-8.2_57  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                     [rob_1_maxVel, con_3, con_2]   \n",
       "100%                 [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                  [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_maxVel]   \n",
       "\n",
       "                                           feeder_3-8.3_58  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                 [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                      [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                       [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "20%                    [rob_2_maxVel, score, rob_1_maxVel]   \n",
       "\n",
       "                                               gripper_1-8.4_59  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                       [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                               max_Vel_2-8.5_60  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                        [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%                      [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                       [rob_2_maxVel, rob_1_maxVel, rob_2_1]   \n",
       "20%                       [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "\n",
       "                                        size_1-8.6_61  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "150%                     [rob_1_maxVel, con_3, con_2]   \n",
       "100%                 [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                  [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_maxVel]   \n",
       "\n",
       "                                           feeder_3-8.7_62  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                 [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                      [rob_1_maxVel, rob_1_2, rob_1_1]   \n",
       "50%                       [cam_3_X, rob_1_maxVel, EoL_2_X]   \n",
       "20%                    [rob_2_maxVel, score, rob_1_maxVel]   \n",
       "\n",
       "                                               gripper_1-8.8_63  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                 [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "20%                  [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "\n",
       "                                               max_Vel_2-8.9_64  \n",
       "Normal Size Percent                                              \n",
       "200%                 [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "150%                        [rob_2_maxVel, rob_1_maxVel, con_3]  \n",
       "100%                      [rob_2_maxVel, rob_1_vacuum, rob_2_1]  \n",
       "50%                       [rob_2_maxVel, rob_1_maxVel, rob_2_1]  \n",
       "20%                       [rob_2_maxVel, rob_1_vacuum, rob_2_1]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_normal_size_HT = pd.DataFrame()\n",
    "result_normal_size_HT = pd.concat([result_HT_normal_size_1,result_HT_normal_size_2,result_HT_normal_size_3,result_HT_normal_size_4,result_HT_normal_size_5], ignore_index=True)\n",
    "result_normal_size_HT['Normal Size Percent'] = pd.DataFrame({'HT':['200%','150%','100%','50%','20%']})\n",
    "result_normal_size_HT.set_index('Normal Size Percent', inplace=True)\n",
    "result_normal_size_HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "fb2f48c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    200%  150%  100%   50%   20%\n",
       "size_1-8.6_61     0.0000  0.00   0.0  0.00  1.00\n",
       "feeder_3-8.7_62   0.0000  1.00   0.0  1.00  0.00\n",
       "gripper_1-8.8_63  1.0000  1.00   1.0  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.0000  1.00   1.0  1.00  1.00\n",
       "Total             0.5625  0.75   0.5  0.75  0.75"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_normal_size_HT,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "92579010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%  150%  100%   50%   20%\n",
       "size_1-8.6_61      0.0  0.00   0.0  0.00  1.00\n",
       "feeder_3-8.7_62    0.0  1.00   0.0  1.00  0.00\n",
       "gripper_1-8.8_63   1.0  1.00   1.0  1.00  1.00\n",
       "max_Vel_2-8.9_64   1.0  1.00   1.0  1.00  1.00\n",
       "Total              0.5  0.75   0.5  0.75  0.75"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_normal_size_HT,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3bc861f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_2_HT.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_2_HT.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e130015",
   "metadata": {},
   "source": [
    "### RW - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e4b550f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW_normal_size_1 = run_RW_normal_size(folder_path,files,startrow=839,normal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_normal_size_2 = run_RW_normal_size(folder_path,files,startrow=839,normal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_normal_size_3 = run_RW_normal_size(folder_path,files,startrow=839,normal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_normal_size_4 = run_RW_normal_size(folder_path,files,startrow=839,normal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_normal_size_5 = run_RW_normal_size(folder_path,files,startrow=839,normal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b1a5ab58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[cam_1_X, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_maxVel, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, con_2, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, cam_1_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, cam_1_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_3_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, cam_3_X]</td>\n",
       "      <td>[cam_3_X, cam_3_Y, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_3_X, cam_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, cam_2_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_3_X, cam_3_Y, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_3_X]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, cam_3_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_2_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_Y, rob_2_supply, cam_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_1_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, con_2]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, con_2]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, con_2]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              size_1-1.2_1  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [cam_1_X, rob_2_supply, rob_1_supply]   \n",
       "150%                      [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "100%                      [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "50%                       [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "20%                       [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "\n",
       "                                                 feeder_3-1.3_2  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_supply, rob_1_supply, rob_2_maxVel]   \n",
       "150%                      [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "100%                           [rob_2_supply, cam_3_X, cam_1_Y]   \n",
       "50%                            [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "20%                       [cam_1_X, rob_1_supply, rob_2_supply]   \n",
       "\n",
       "                                           gripper_1-1.4_3  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "150%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%                  [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                                max_Vel_2-1.5_4  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "150%                 [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "100%                           [rob_2_maxVel, cam_2_Y, cam_3_X]   \n",
       "50%                       [rob_2_maxVel, rob_1_supply, cam_1_X]   \n",
       "20%                            [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                         size_1-1.6_5  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "150%                 [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "100%                      [cam_1_Y, cam_1_X, cam_3_X]   \n",
       "50%                  [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "\n",
       "                                            feeder_3-1.7_6  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                 [rob_2_supply, cam_1_X, rob_1_supply]   \n",
       "100%                           [cam_3_X, cam_3_Y, cam_1_X]   \n",
       "50%                       [cam_1_X, rob_2_supply, cam_1_Y]   \n",
       "20%                              [cam_1_X, cam_1_Y, con_2]   \n",
       "\n",
       "                                           gripper_1-1.8_7  \\\n",
       "Normal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, con_2]   \n",
       "150%                 [rob_1_supply, cam_1_X, rob_2_maxVel]   \n",
       "100%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%                  [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                                max_Vel_2-1.9_8  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "150%                      [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "100%                      [rob_2_maxVel, cam_3_X, rob_2_supply]   \n",
       "50%                       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "20%                            [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                         size_1-2.2_9  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "150%                 [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "100%                 [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "50%                  [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "20%                         [cam_1_X, cam_1_Y, con_2]   \n",
       "\n",
       "                                           feeder_3-2.3_10  ...  \\\n",
       "Normal Size Percent                                         ...   \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_Y]  ...   \n",
       "150%                 [rob_1_supply, rob_2_supply, cam_1_Y]  ...   \n",
       "100%                      [rob_2_supply, cam_3_X, cam_3_Y]  ...   \n",
       "50%                       [cam_1_Y, rob_2_supply, cam_1_X]  ...   \n",
       "20%                         [cam_1_X, rob_1_supply, con_2]  ...   \n",
       "\n",
       "                                          gripper_1-7.8_55  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "100%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%                  [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                               max_Vel_2-7.9_56  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_1_supply, rob_2_maxVel, rob_2_supply]   \n",
       "150%                 [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "100%                           [rob_2_maxVel, cam_3_X, cam_2_Y]   \n",
       "50%                       [rob_2_maxVel, rob_1_supply, cam_1_X]   \n",
       "20%                            [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                        size_1-8.2_57  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "150%                 [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "100%                 [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "50%                  [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "20%                         [cam_1_Y, cam_1_X, con_2]   \n",
       "\n",
       "                                           feeder_3-8.3_58  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "150%                 [rob_1_supply, cam_1_Y, rob_2_maxVel]   \n",
       "100%                      [cam_3_X, cam_3_Y, rob_1_supply]   \n",
       "50%                  [rob_1_supply, cam_1_Y, rob_2_supply]   \n",
       "20%                       [cam_1_X, rob_1_supply, cam_1_Y]   \n",
       "\n",
       "                                          gripper_1-8.4_59  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                 [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%                  [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                               max_Vel_2-8.5_60  \\\n",
       "Normal Size Percent                                               \n",
       "200%                 [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "150%                      [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "100%                      [rob_2_supply, rob_2_maxVel, cam_3_X]   \n",
       "50%                       [rob_2_maxVel, cam_1_X, rob_2_supply]   \n",
       "20%                            [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                        size_1-8.6_61  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "150%                 [cam_1_Y, cam_1_X, rob_2_maxVel]   \n",
       "100%                      [cam_1_Y, cam_1_X, cam_3_X]   \n",
       "50%                  [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "20%                  [cam_1_X, cam_1_Y, rob_1_supply]   \n",
       "\n",
       "                                           feeder_3-8.7_62  \\\n",
       "Normal Size Percent                                          \n",
       "200%                 [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "150%                 [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "100%                 [rob_2_supply, rob_1_supply, cam_3_Y]   \n",
       "50%                  [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "20%                       [cam_1_X, rob_1_supply, cam_1_Y]   \n",
       "\n",
       "                                          gripper_1-8.8_63  \\\n",
       "Normal Size Percent                                          \n",
       "200%                   [rob_1_supply, con_2, rob_2_supply]   \n",
       "150%                 [rob_1_supply, rob_2_maxVel, cam_1_X]   \n",
       "100%                 [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "20%                       [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                                          max_Vel_2-8.9_64  \n",
       "Normal Size Percent                                         \n",
       "200%                 [rob_2_maxVel, rob_1_supply, cam_1_X]  \n",
       "150%                      [rob_2_maxVel, cam_1_Y, cam_1_X]  \n",
       "100%                 [rob_2_maxVel, cam_3_X, rob_2_supply]  \n",
       "50%                  [rob_2_maxVel, cam_1_X, rob_1_supply]  \n",
       "20%                       [rob_2_maxVel, cam_1_X, cam_1_Y]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_normal_size_RW = pd.DataFrame()\n",
    "result_normal_size_RW = pd.concat([result_RW_normal_size_1,result_RW_normal_size_2,result_RW_normal_size_3,result_RW_normal_size_4,result_RW_normal_size_5], ignore_index=True)\n",
    "result_normal_size_RW['Normal Size Percent'] = pd.DataFrame({'RW':['200%','150%','100%','50%','20%']})\n",
    "result_normal_size_RW.set_index('Normal Size Percent', inplace=True)\n",
    "result_normal_size_RW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4957d7fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.78125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     200%  150%  100%   50%   20%\n",
       "size_1-8.6_61     1.00000  1.00   1.0  1.00  1.00\n",
       "feeder_3-8.7_62   0.00000  0.00   1.0  0.00  0.00\n",
       "gripper_1-8.8_63  1.00000  1.00   1.0  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.00000  1.00   1.0  1.00  1.00\n",
       "Total             0.78125  0.75   1.0  0.75  0.75"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_normal_size_RW,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a21b3feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.71875</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%     150%   100%   50%   20%\n",
       "size_1-8.6_61     1.000000  1.00000  1.000  1.00  1.00\n",
       "feeder_3-8.7_62   0.000000  0.00000  0.000  0.00  0.00\n",
       "gripper_1-8.8_63  1.000000  1.00000  1.000  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.000000  1.00000  1.000  1.00  1.00\n",
       "Total             0.484375  0.71875  0.625  0.75  0.75"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_normal_size_RW,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7de5b17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_2_RW.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_2_RW.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff6b904",
   "metadata": {},
   "source": [
    "### RCD - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "abaa15c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RCD_normal_size_1 = run_RCD_normal_size(folder_path,files,startrow=839,normal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_normal_size_2 = run_RCD_normal_size(folder_path,files,startrow=839,normal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_normal_size_3 = run_RCD_normal_size(folder_path,files,startrow=839,normal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_normal_size_4 = run_RCD_normal_size(folder_path,files,startrow=839,normal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_normal_size_5 = run_RCD_normal_size(folder_path,files,startrow=839,normal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3acea0e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[EoL_4_X, cam_1_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_4_X, EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_3_X, cam_3_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_4_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_2_Y, EoL_5_X, EoL_2_X]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_6_X]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[cam_1_X, EoL_6_X, EoL_3_X]</td>\n",
       "      <td>[EoL_5_X, cam_3_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_4_Y, EoL_2_X]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[EoL_5_X, EoL_6_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_2_Y, rob_1_supply]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[EoL_5_X, cam_1_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_4_X, cam_3_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_4_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[cam_3_X, EoL_6_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_4_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_4_X, EoL_5_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_5_X, EoL_4_X]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_2_X, rob_1_supply]</td>\n",
       "      <td>[EoL_6_X, rob_2_maxVel, EoL_6_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[EoL_6_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_6_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_X, EoL_4_Y]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[EoL_4_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, EoL_2_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_3_X, EoL_2_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_5_Y, EoL_4_Y, EoL_3_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_3_X, cam_3_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_X, EoL_2_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_1_Y, EoL_5_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_4_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_X, EoL_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_5_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_3_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_4_Y, cam_1_Y, EoL_3_Y]</td>\n",
       "      <td>[cam_3_X, EoL_6_Y, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, EoL_4_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_2_X, rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[EoL_4_X, EoL_5_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_5_X, EoL_2_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_6_Y, cam_3_X]</td>\n",
       "      <td>[EoL_6_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_4_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_X]</td>\n",
       "      <td>[EoL_6_Y, cam_3_X]</td>\n",
       "      <td>[cam_1_X, EoL_6_X, EoL_2_X]</td>\n",
       "      <td>[cam_3_X, cam_3_Y, EoL_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_1_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_5_X, cam_1_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_X, EoL_2_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_2_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_X]</td>\n",
       "      <td>[cam_1_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_2_X, EoL_5_X]</td>\n",
       "      <td>[EoL_5_X, EoL_6_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_5_X, cam_2_Y]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    size_1-1.2_1               feeder_3-1.3_2  \\\n",
       "Normal Size Percent                                                             \n",
       "200%                          [EoL_4_X, cam_1_X]           [EoL_6_Y, EoL_2_X]   \n",
       "150%                 [EoL_5_X, EoL_6_Y, EoL_6_X]           [EoL_6_X, EoL_6_Y]   \n",
       "100%                          [EoL_6_Y, cam_1_Y]  [EoL_4_X, EoL_6_Y, EoL_2_Y]   \n",
       "50%                  [cam_1_Y, EoL_5_Y, EoL_6_Y]           [EoL_5_X, EoL_2_Y]   \n",
       "20%                  [EoL_4_X, EoL_5_Y, EoL_3_X]  [EoL_5_X, EoL_2_X, EoL_2_Y]   \n",
       "\n",
       "                                      gripper_1-1.4_3  \\\n",
       "Normal Size Percent                                     \n",
       "200%                               [EoL_3_Y, EoL_6_Y]   \n",
       "150%                 [EoL_4_X, EoL_2_Y, rob_1_supply]   \n",
       "100%                      [EoL_6_Y, EoL_3_X, EoL_4_Y]   \n",
       "50%                                [EoL_4_Y, EoL_2_Y]   \n",
       "20%                                [EoL_6_Y, cam_3_X]   \n",
       "\n",
       "                             max_Vel_2-1.5_4                 size_1-1.6_5  \\\n",
       "Normal Size Percent                                                         \n",
       "200%                               [EoL_6_X]  [EoL_4_X, EoL_6_X, EoL_6_Y]   \n",
       "150%                          [rob_2_maxVel]           [EoL_5_X, cam_1_Y]   \n",
       "100%                          [rob_2_maxVel]           [EoL_4_X, EoL_4_Y]   \n",
       "50%                                [EoL_6_Y]           [cam_1_Y, EoL_5_X]   \n",
       "20%                  [EoL_6_X, rob_2_maxVel]           [EoL_3_X, EoL_5_Y]   \n",
       "\n",
       "                                  feeder_3-1.7_6              gripper_1-1.8_7  \\\n",
       "Normal Size Percent                                                             \n",
       "200%                 [EoL_4_X, EoL_3_X, cam_3_Y]  [EoL_2_Y, EoL_4_Y, EoL_5_Y]   \n",
       "150%                 [EoL_6_X, EoL_4_X, cam_3_X]  [EoL_4_Y, EoL_4_X, EoL_5_Y]   \n",
       "100%                          [EoL_4_X, EoL_3_Y]           [EoL_2_Y, EoL_5_Y]   \n",
       "50%                           [EoL_4_X, EoL_2_Y]           [EoL_2_Y, EoL_5_Y]   \n",
       "20%                  [EoL_5_Y, EoL_4_X, EoL_3_Y]           [EoL_3_X, cam_3_X]   \n",
       "\n",
       "                             max_Vel_2-1.9_8                 size_1-2.2_9  \\\n",
       "Normal Size Percent                                                         \n",
       "200%                               [EoL_6_Y]  [EoL_3_Y, EoL_6_Y, EoL_3_X]   \n",
       "150%                               [EoL_6_Y]           [EoL_6_X, EoL_6_Y]   \n",
       "100%                 [EoL_6_X, rob_2_maxVel]  [EoL_6_Y, EoL_6_X, EoL_4_Y]   \n",
       "50%                                [EoL_6_Y]           [cam_1_Y, EoL_4_X]   \n",
       "20%                       [EoL_6_Y, cam_3_X]  [cam_1_X, EoL_6_X, EoL_2_X]   \n",
       "\n",
       "                                 feeder_3-2.3_10  ...  \\\n",
       "Normal Size Percent                               ...   \n",
       "200%                          [EoL_5_Y, EoL_6_Y]  ...   \n",
       "150%                          [cam_3_X, EoL_6_Y]  ...   \n",
       "100%                          [EoL_3_Y, EoL_6_X]  ...   \n",
       "50%                  [EoL_6_Y, EoL_3_X, EoL_3_Y]  ...   \n",
       "20%                  [cam_3_X, cam_3_Y, EoL_3_X]  ...   \n",
       "\n",
       "                                     gripper_1-7.8_55  \\\n",
       "Normal Size Percent                                     \n",
       "200%                      [EoL_2_Y, EoL_5_X, EoL_2_X]   \n",
       "150%                               [EoL_4_X, EoL_2_Y]   \n",
       "100%                 [rob_1_supply, EoL_2_Y, EoL_6_X]   \n",
       "50%                                [EoL_5_Y, EoL_5_X]   \n",
       "20%                                [EoL_1_Y, EoL_2_Y]   \n",
       "\n",
       "                                     max_Vel_2-7.9_56  \\\n",
       "Normal Size Percent                                     \n",
       "200%                               [EoL_6_X, EoL_6_Y]   \n",
       "150%                               [EoL_6_X, EoL_6_Y]   \n",
       "100%                          [EoL_6_Y, rob_2_maxVel]   \n",
       "50%                                         [EoL_6_Y]   \n",
       "20%                  [EoL_2_X, cam_3_X, rob_2_maxVel]   \n",
       "\n",
       "                                   size_1-8.2_57              feeder_3-8.3_58  \\\n",
       "Normal Size Percent                                                             \n",
       "200%                          [EoL_4_X, EoL_6_X]  [EoL_3_X, EoL_5_Y, EoL_4_Y]   \n",
       "150%                 [cam_1_Y, EoL_4_X, EoL_5_X]  [EoL_6_Y, EoL_5_X, EoL_4_X]   \n",
       "100%                 [EoL_5_Y, EoL_6_Y, EoL_3_X]  [EoL_3_X, EoL_2_Y, EoL_2_X]   \n",
       "50%                           [EoL_5_Y, EoL_5_X]           [EoL_2_Y, EoL_3_X]   \n",
       "20%                           [EoL_5_X, cam_1_X]  [EoL_2_Y, EoL_5_X, EoL_2_X]   \n",
       "\n",
       "                                     gripper_1-8.4_59  \\\n",
       "Normal Size Percent                                     \n",
       "200%                 [rob_1_supply, EoL_4_Y, EoL_2_Y]   \n",
       "150%                               [EoL_3_X, EoL_5_Y]   \n",
       "100%                      [EoL_5_Y, EoL_4_Y, EoL_3_X]   \n",
       "50%                                [EoL_3_X, EoL_6_Y]   \n",
       "20%                                [EoL_4_Y, EoL_2_X]   \n",
       "\n",
       "                            max_Vel_2-8.5_60                size_1-8.6_61  \\\n",
       "Normal Size Percent                                                         \n",
       "200%                               [EoL_6_X]  [cam_1_X, EoL_6_X, EoL_3_X]   \n",
       "150%                               [EoL_6_Y]           [EoL_6_Y, cam_1_Y]   \n",
       "100%                 [rob_2_maxVel, EoL_6_Y]           [EoL_5_Y, EoL_6_Y]   \n",
       "50%                                [EoL_6_Y]  [EoL_4_Y, cam_1_Y, EoL_3_Y]   \n",
       "20%                  [rob_2_maxVel, EoL_6_X]           [cam_1_Y, EoL_3_X]   \n",
       "\n",
       "                                 feeder_3-8.7_62  \\\n",
       "Normal Size Percent                                \n",
       "200%                          [EoL_5_X, cam_3_Y]   \n",
       "150%                          [EoL_5_Y, EoL_2_X]   \n",
       "100%                 [EoL_2_Y, EoL_3_X, cam_3_Y]   \n",
       "50%                  [cam_3_X, EoL_6_Y, EoL_5_Y]   \n",
       "20%                           [EoL_2_X, EoL_5_X]   \n",
       "\n",
       "                                     gripper_1-8.8_63  \\\n",
       "Normal Size Percent                                     \n",
       "200%                      [EoL_5_X, EoL_4_Y, EoL_2_X]   \n",
       "150%                          [EoL_2_X, rob_1_supply]   \n",
       "100%                               [EoL_3_Y, EoL_4_X]   \n",
       "50%                  [rob_1_supply, EoL_4_X, EoL_3_Y]   \n",
       "20%                       [EoL_5_X, EoL_6_Y, EoL_3_Y]   \n",
       "\n",
       "                                     max_Vel_2-8.9_64  \n",
       "Normal Size Percent                                    \n",
       "200%                                   [rob_2_maxVel]  \n",
       "150%                 [EoL_6_X, rob_2_maxVel, EoL_6_Y]  \n",
       "100%                               [EoL_6_X, EoL_2_X]  \n",
       "50%                           [EoL_2_X, rob_2_maxVel]  \n",
       "20%                                [EoL_5_X, cam_2_Y]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_normal_size_RCD = pd.DataFrame()\n",
    "result_normal_size_RCD = pd.concat([result_RCD_normal_size_1,result_RCD_normal_size_2,result_RCD_normal_size_3,result_RCD_normal_size_4,result_RCD_normal_size_5], ignore_index=True)\n",
    "result_normal_size_RCD['Normal Size Percent'] = pd.DataFrame({'RCD':['200%','150%','100%','50%','20%']})\n",
    "result_normal_size_RCD.set_index('Normal Size Percent', inplace=True)\n",
    "result_normal_size_RCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e5a3f832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.421875</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.328125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%  150%      100%       50%       20%\n",
       "size_1-8.6_61     1.000000   1.0  0.000000  1.000000  1.000000\n",
       "feeder_3-8.7_62   1.000000   0.0  1.000000  1.000000  0.000000\n",
       "gripper_1-8.8_63  0.000000   1.0  0.000000  1.000000  0.000000\n",
       "max_Vel_2-8.9_64  1.000000   1.0  0.000000  1.000000  0.000000\n",
       "Total             0.359375   0.5  0.421875  0.296875  0.328125"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_normal_size_RCD,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8759dadf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.21875</td>\n",
       "      <td>0.171875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%      150%      100%      50%       20%\n",
       "size_1-8.6_61     1.000000  0.000000  0.000000  0.00000  1.000000\n",
       "feeder_3-8.7_62   0.000000  0.000000  0.000000  1.00000  0.000000\n",
       "gripper_1-8.8_63  0.000000  0.000000  0.000000  1.00000  0.000000\n",
       "max_Vel_2-8.9_64  1.000000  0.000000  0.000000  0.00000  0.000000\n",
       "Total             0.171875  0.296875  0.171875  0.21875  0.171875"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_normal_size_RCD,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1bbd6cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_2_RCD.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_2_RCD.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7709338",
   "metadata": {},
   "source": [
    "## Variation 3 - Abnormal Size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c73ac5",
   "metadata": {},
   "source": [
    "The ED-algorithm requires the same length for the normal and abnormal data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0fcbb3",
   "metadata": {},
   "source": [
    "### HT - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7586150e",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_HT_abnormal_size_1 = run_HT_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_abnormal_size_2 = run_HT_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_abnormal_size_3 = run_HT_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_abnormal_size_4 = run_HT_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_abnormal_size_5 = run_HT_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "5026ed97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abnormal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, EoL_4_Y, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_4_Y, cam_3_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_1_1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           size_1-1.2_1  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                   [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "150%                   [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "100%                        [rob_1_1, rob_1_3, rob_1_2]   \n",
       "50%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "\n",
       "                                              feeder_3-1.3_2  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "100%                        [rob_2_maxVel, EoL_4_Y, cam_3_X]   \n",
       "50%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "20%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "\n",
       "                                                  gripper_1-1.4_3  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                        [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "50%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                                  max_Vel_2-1.5_4  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "100%                        [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "50%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                           size_1-1.6_5  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                   [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                   [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "100%                        [rob_1_1, rob_1_3, rob_1_2]   \n",
       "50%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "\n",
       "                                              feeder_3-1.7_6  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "100%                        [rob_2_maxVel, EoL_4_Y, cam_3_X]   \n",
       "50%                         [rob_2_maxVel, EoL_4_Y, cam_3_X]   \n",
       "20%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "\n",
       "                                                  gripper_1-1.8_7  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                                  max_Vel_2-1.9_8  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "100%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                           size_1-2.2_9  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                   [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                   [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "100%                   [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "50%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "\n",
       "                                             feeder_3-2.3_10  ...  \\\n",
       "abnormal Size Percent                                         ...   \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "100%                        [rob_2_maxVel, EoL_4_Y, cam_3_X]  ...   \n",
       "50%                              [EoL_4_Y, cam_3_X, EoL_5_Y]  ...   \n",
       "20%                              [EoL_4_Y, cam_3_X, EoL_5_Y]  ...   \n",
       "\n",
       "                                                 gripper_1-7.8_55  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                                 max_Vel_2-7.9_56  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "100%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "50%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                          size_1-8.2_57  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                   [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%                   [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "100%                        [rob_1_1, rob_1_3, rob_1_2]   \n",
       "50%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "\n",
       "                                             feeder_3-8.3_58  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "100%                        [rob_2_maxVel, EoL_4_Y, cam_3_X]   \n",
       "50%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "20%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "\n",
       "                                            gripper_1-8.4_59  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "150%                   [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "100%                   [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "50%                    [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                    [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                                 max_Vel_2-8.5_60  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "100%                        [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "50%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                          size_1-8.6_61  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                   [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "150%                   [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "100%                   [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "50%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%                         [rob_1_1, rob_1_3, rob_1_2]   \n",
       "\n",
       "                                             feeder_3-8.7_62  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "100%                        [rob_2_maxVel, EoL_4_Y, cam_3_X]   \n",
       "50%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "20%                              [EoL_4_Y, cam_3_X, EoL_5_Y]   \n",
       "\n",
       "                                                 gripper_1-8.8_63  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%                   [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%                         [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                                 max_Vel_2-8.9_64  \n",
       "abnormal Size Percent                                              \n",
       "200%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "150%                   [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "100%                        [rob_2_maxVel, rob_1_vacuum, rob_1_1]  \n",
       "50%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]  \n",
       "20%                         [rob_2_maxVel, rob_1_vacuum, rob_1_1]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_abnormal_size_HT = pd.DataFrame()\n",
    "result_abnormal_size_HT = pd.concat([result_HT_abnormal_size_1,result_HT_abnormal_size_2,result_HT_abnormal_size_3,result_HT_abnormal_size_4,result_HT_abnormal_size_5], ignore_index=True)\n",
    "result_abnormal_size_HT['abnormal Size Percent'] = pd.DataFrame({'HT':['200%','150%','100%','50%','20%']})\n",
    "result_abnormal_size_HT.set_index('abnormal Size Percent', inplace=True)\n",
    "result_abnormal_size_HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4640c262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%      150%  100%   50%   20%\n",
       "size_1-8.6_61     0.000000  0.000000  0.00  0.00  0.00\n",
       "feeder_3-8.7_62   0.000000  0.000000  1.00  1.00  1.00\n",
       "gripper_1-8.8_63  1.000000  1.000000  1.00  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.000000  1.000000  1.00  1.00  1.00\n",
       "Total             0.578125  0.578125  0.75  0.75  0.75"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_abnormal_size_HT,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b5bbdb0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%  150%  100%  50%  20%\n",
       "size_1-8.6_61      0.0   0.0   0.0  0.0  0.0\n",
       "feeder_3-8.7_62    0.0   0.0   0.0  0.0  0.0\n",
       "gripper_1-8.8_63   1.0   1.0   1.0  1.0  1.0\n",
       "max_Vel_2-8.9_64   1.0   1.0   1.0  1.0  1.0\n",
       "Total              0.5   0.5   0.5  0.5  0.5"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_abnormal_size_HT,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5aae35a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_3_HT.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_3_HT.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8926c004",
   "metadata": {},
   "source": [
    "### RW - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "11042b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW_abnormal_size_1 = run_RW_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_abnormal_size_2 = run_RW_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_abnormal_size_3 = run_RW_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_abnormal_size_4 = run_RW_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_abnormal_size_5 = run_RW_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a22f95b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abnormal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, con_2, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_2_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[rob_1_supply, cam_2_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_2_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_2_Y]</td>\n",
       "      <td>[cam_3_X, cam_2_Y, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_2_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_2_Y]</td>\n",
       "      <td>[rob_2_supply, cam_2_X, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, cam_2_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_2_Y]</td>\n",
       "      <td>[rob_2_supply, cam_2_X, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_2_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, cam_2_X]</td>\n",
       "      <td>[rob_2_supply, cam_2_Y, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_2_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_2_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_2_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, rob_1_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, con_2, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_supply, con_1]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, con_2]</td>\n",
       "      <td>[rob_1_supply, con_2, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[con_2, rob_2_supply, rob_1_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, con_2]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, con_2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                size_1-1.2_1  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "150%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "100%                   [rob_1_supply, cam_2_Y, rob_2_supply]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_2_supply, con_2, rob_1_supply]   \n",
       "\n",
       "                                              feeder_3-1.3_2  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_supply, rob_1_supply, cam_3_X]   \n",
       "150%                   [rob_2_supply, rob_1_supply, cam_3_X]   \n",
       "100%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "50%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                             gripper_1-1.4_3  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                   [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_2_X]   \n",
       "50%                    [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                                  max_Vel_2-1.5_4  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "150%                        [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "100%                        [rob_2_supply, rob_2_maxVel, cam_2_Y]   \n",
       "50%                    [rob_2_maxVel, rob_1_supply, rob_2_supply]   \n",
       "20%                           [rob_2_maxVel, con_2, rob_1_supply]   \n",
       "\n",
       "                                                size_1-1.6_5  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "150%                   [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "100%                               [cam_3_X, cam_2_Y, con_2]   \n",
       "50%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "20%                      [rob_2_supply, con_2, rob_1_supply]   \n",
       "\n",
       "                                              feeder_3-1.7_6  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "150%                   [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "100%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                             gripper_1-1.8_7  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_2_Y]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                                  max_Vel_2-1.9_8  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "150%                        [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "100%                        [rob_2_maxVel, rob_2_supply, cam_2_Y]   \n",
       "50%                    [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "20%                           [rob_2_maxVel, rob_1_supply, con_1]   \n",
       "\n",
       "                                                size_1-2.2_9  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                          [rob_2_supply, con_2, cam_1_X]   \n",
       "150%                          [rob_2_supply, cam_1_Y, con_2]   \n",
       "100%                        [rob_2_supply, cam_2_X, cam_1_X]   \n",
       "50%                    [rob_2_supply, rob_1_supply, cam_2_X]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                             feeder_3-2.3_10  ...  \\\n",
       "abnormal Size Percent                                         ...   \n",
       "200%                     [rob_1_supply, rob_2_supply, con_2]  ...   \n",
       "150%                     [rob_1_supply, rob_2_supply, con_2]  ...   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_3_X]  ...   \n",
       "50%                      [rob_2_supply, rob_1_supply, con_2]  ...   \n",
       "20%                      [rob_1_supply, rob_2_supply, con_2]  ...   \n",
       "\n",
       "                                            gripper_1-7.8_55  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "150%                   [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "100%                   [rob_1_supply, cam_2_Y, rob_2_supply]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                                 max_Vel_2-7.9_56  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "150%                   [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "100%                        [rob_2_maxVel, rob_2_supply, cam_2_Y]   \n",
       "50%                    [rob_2_maxVel, rob_1_supply, rob_2_supply]   \n",
       "20%                                [rob_2_maxVel, cam_1_X, con_2]   \n",
       "\n",
       "                                               size_1-8.2_57  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "150%                   [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%                   [rob_2_supply, cam_2_X, rob_1_supply]   \n",
       "50%                    [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%                           [rob_1_supply, con_2, cam_1_X]   \n",
       "\n",
       "                                             feeder_3-8.3_58  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%                   [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                            gripper_1-8.4_59  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, cam_2_X]   \n",
       "150%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_2_X]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                                 max_Vel_2-8.5_60  \\\n",
       "abnormal Size Percent                                               \n",
       "200%                   [rob_2_maxVel, rob_2_supply, rob_1_supply]   \n",
       "150%                        [rob_2_maxVel, cam_1_Y, rob_1_supply]   \n",
       "100%                             [rob_2_maxVel, cam_1_Y, cam_2_X]   \n",
       "50%                    [rob_2_supply, rob_2_maxVel, rob_1_supply]   \n",
       "20%                         [rob_2_maxVel, cam_1_Y, rob_2_supply]   \n",
       "\n",
       "                                               size_1-8.6_61  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "150%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "100%                   [rob_2_supply, cam_2_Y, rob_1_supply]   \n",
       "50%                    [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%                      [con_2, rob_2_supply, rob_1_supply]   \n",
       "\n",
       "                                             feeder_3-8.7_62  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                   [rob_1_supply, rob_2_supply, cam_3_Y]   \n",
       "150%                     [rob_2_supply, rob_1_supply, con_2]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "50%                    [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%                      [rob_2_supply, rob_1_supply, con_2]   \n",
       "\n",
       "                                            gripper_1-8.8_63  \\\n",
       "abnormal Size Percent                                          \n",
       "200%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "150%                     [rob_1_supply, rob_2_supply, con_2]   \n",
       "100%                   [rob_1_supply, rob_2_supply, cam_2_X]   \n",
       "50%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "20%                      [rob_1_supply, rob_2_supply, con_2]   \n",
       "\n",
       "                                                 max_Vel_2-8.9_64  \n",
       "abnormal Size Percent                                              \n",
       "200%                   [rob_2_maxVel, rob_2_supply, rob_1_supply]  \n",
       "150%                        [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "100%                        [rob_2_maxVel, rob_2_supply, cam_2_X]  \n",
       "50%                    [rob_2_maxVel, rob_2_supply, rob_1_supply]  \n",
       "20%                                [rob_2_maxVel, cam_1_Y, con_2]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_abnormal_size_RW = pd.DataFrame()\n",
    "result_abnormal_size_RW = pd.concat([result_RW_abnormal_size_1,result_RW_abnormal_size_2,result_RW_abnormal_size_3,result_RW_abnormal_size_4,result_RW_abnormal_size_5], ignore_index=True)\n",
    "result_abnormal_size_RW['abnormal Size Percent'] = pd.DataFrame({'RW':['200%','150%','100%','50%','20%']})\n",
    "result_abnormal_size_RW.set_index('abnormal Size Percent', inplace=True)\n",
    "result_abnormal_size_RW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a96dce0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.765625</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.53125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%      150%     100%     50%      20%\n",
       "size_1-8.6_61     1.000000  0.000000  0.00000  1.0000  0.00000\n",
       "feeder_3-8.7_62   1.000000  0.000000  1.00000  0.0000  0.00000\n",
       "gripper_1-8.8_63  1.000000  1.000000  1.00000  1.0000  1.00000\n",
       "max_Vel_2-8.9_64  1.000000  1.000000  1.00000  1.0000  1.00000\n",
       "Total             0.765625  0.703125  0.59375  0.5625  0.53125"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_abnormal_size_RW,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "07538333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.484375</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%      150%      100%       50%    20%\n",
       "size_1-8.6_61      0.0  0.000000  0.000000  0.000000  0.000\n",
       "feeder_3-8.7_62    0.0  0.000000  0.000000  0.000000  0.000\n",
       "gripper_1-8.8_63   1.0  1.000000  1.000000  1.000000  1.000\n",
       "max_Vel_2-8.9_64   1.0  1.000000  1.000000  1.000000  1.000\n",
       "Total              0.5  0.453125  0.484375  0.484375  0.375"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_abnormal_size_RW,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b5e4a3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_3_RW.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_3_RW.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb872b44",
   "metadata": {},
   "source": [
    "### RCD - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "368bd34b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RCD_abnormal_size_1 = run_RCD_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=2.00,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_abnormal_size_2 = run_RCD_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_abnormal_size_3 = run_RCD_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_abnormal_size_4 = run_RCD_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_abnormal_size_5 = run_RCD_abnormal_size(folder_path,files,startrow=839,abnormal_size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "235196ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abnormal Size Percent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[EoL_6_X, EoL_3_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_4_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_X]</td>\n",
       "      <td>[cam_3_X, EoL_5_X]</td>\n",
       "      <td>[EoL_6_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel]</td>\n",
       "      <td>[cam_1_X, EoL_5_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, EoL_6_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_5_X, EoL_2_X, EoL_4_Y]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[EoL_3_X, EoL_4_X, cam_1_X]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_5_X, EoL_4_X]</td>\n",
       "      <td>[EoL_4_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[EoL_5_Y, cam_1_X]</td>\n",
       "      <td>[EoL_3_X, EoL_6_Y, cam_3_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_4_X, cam_1_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_3_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_5_Y, EoL_3_Y, EoL_4_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_5_Y, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_3_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_5_X, cam_3_X]</td>\n",
       "      <td>[EoL_4_X, EoL_2_Y, EoL_6_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_X]</td>\n",
       "      <td>[cam_1_Y, EoL_6_Y, EoL_5_X]</td>\n",
       "      <td>[cam_3_X, EoL_4_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_4_X, EoL_3_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[EoL_4_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_4_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_5_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_6_X, EoL_5_X]</td>\n",
       "      <td>[EoL_5_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_3_Y, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[cam_3_X, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_2_X, EoL_6_X, EoL_5_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_5_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_6_X, cam_3_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_4_Y]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[EoL_3_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_X, EoL_5_X]</td>\n",
       "      <td>[EoL_4_X, EoL_6_X]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_1_Y, EoL_3_X, cam_1_X]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel]</td>\n",
       "      <td>[EoL_5_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_X, EoL_2_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_5_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_4_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_6_X, EoL_5_X, EoL_3_Y]</td>\n",
       "      <td>[cam_2_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_5_Y, cam_3_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_3_X, EoL_2_X, rob_1_supply]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[EoL_5_X, cam_1_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[cam_3_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_2_X]</td>\n",
       "      <td>[EoL_4_X, EoL_3_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_2_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_3_X, EoL_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_5_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_4_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_5_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_4_Y]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_X]</td>\n",
       "      <td>[cam_1_X, EoL_3_X]</td>\n",
       "      <td>[EoL_5_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X, cam_2_X]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      size_1-1.2_1  \\\n",
       "abnormal Size Percent                                \n",
       "200%                   [EoL_6_X, EoL_3_X, EoL_6_Y]   \n",
       "150%                            [EoL_5_Y, cam_1_X]   \n",
       "100%                            [EoL_4_X, EoL_4_Y]   \n",
       "50%                    [cam_1_Y, EoL_3_X, cam_1_X]   \n",
       "20%                             [EoL_5_X, cam_1_Y]   \n",
       "\n",
       "                                    feeder_3-1.3_2  \\\n",
       "abnormal Size Percent                                \n",
       "200%                            [EoL_3_X, EoL_4_Y]   \n",
       "150%                   [EoL_3_X, EoL_6_Y, cam_3_Y]   \n",
       "100%                   [EoL_2_Y, EoL_4_X, EoL_6_Y]   \n",
       "50%                    [EoL_3_X, EoL_5_Y, EoL_3_Y]   \n",
       "20%                             [EoL_3_Y, EoL_4_X]   \n",
       "\n",
       "                                   gripper_1-1.4_3  \\\n",
       "abnormal Size Percent                                \n",
       "200%                            [EoL_6_Y, EoL_4_X]   \n",
       "150%                            [EoL_3_X, EoL_3_Y]   \n",
       "100%                   [EoL_3_X, EoL_5_X, EoL_5_Y]   \n",
       "50%                             [EoL_3_X, EoL_5_Y]   \n",
       "20%                             [cam_3_Y, EoL_5_Y]   \n",
       "\n",
       "                                   max_Vel_2-1.5_4  \\\n",
       "abnormal Size Percent                                \n",
       "200%                       [rob_2_maxVel, EoL_6_Y]   \n",
       "150%                                     [EoL_6_Y]   \n",
       "100%                       [EoL_6_X, rob_2_maxVel]   \n",
       "50%                                      [EoL_6_X]   \n",
       "20%                    [EoL_6_Y, EoL_6_X, EoL_5_Y]   \n",
       "\n",
       "                                      size_1-1.6_5  \\\n",
       "abnormal Size Percent                                \n",
       "200%                                     [EoL_3_X]   \n",
       "150%                   [EoL_6_Y, EoL_4_X, cam_1_Y]   \n",
       "100%                            [EoL_6_X, EoL_5_X]   \n",
       "50%                    [EoL_4_Y, EoL_3_Y, EoL_4_X]   \n",
       "20%                             [EoL_6_X, EoL_3_Y]   \n",
       "\n",
       "                                    feeder_3-1.7_6  \\\n",
       "abnormal Size Percent                                \n",
       "200%                            [cam_3_X, EoL_5_X]   \n",
       "150%                   [EoL_2_X, EoL_3_Y, EoL_3_X]   \n",
       "100%                            [EoL_5_X, EoL_5_Y]   \n",
       "50%                             [EoL_6_X, EoL_6_Y]   \n",
       "20%                             [EoL_6_X, EoL_2_X]   \n",
       "\n",
       "                                   gripper_1-1.8_7          max_Vel_2-1.9_8  \\\n",
       "abnormal Size Percent                                                         \n",
       "200%                            [EoL_6_X, EoL_3_Y]  [EoL_6_Y, rob_2_maxVel]   \n",
       "150%                   [EoL_5_Y, EoL_3_Y, EoL_4_Y]                [EoL_6_X]   \n",
       "100%                            [EoL_3_Y, EoL_4_X]                [EoL_6_X]   \n",
       "50%                             [EoL_2_X, EoL_6_Y]  [EoL_6_Y, rob_2_maxVel]   \n",
       "20%                    [EoL_4_X, EoL_3_X, EoL_4_Y]       [EoL_3_Y, EoL_6_Y]   \n",
       "\n",
       "                                      size_1-2.2_9  \\\n",
       "abnormal Size Percent                                \n",
       "200%                   [cam_1_X, EoL_5_Y, EoL_6_X]   \n",
       "150%                            [EoL_4_Y, EoL_3_Y]   \n",
       "100%                   [EoL_3_Y, cam_1_Y, cam_1_X]   \n",
       "50%                             [EoL_5_Y, EoL_3_Y]   \n",
       "20%                             [EoL_2_Y, EoL_4_X]   \n",
       "\n",
       "                                   feeder_3-2.3_10  ...  \\\n",
       "abnormal Size Percent                               ...   \n",
       "200%                   [EoL_6_Y, EoL_2_X, EoL_6_X]  ...   \n",
       "150%                            [EoL_5_Y, cam_3_X]  ...   \n",
       "100%                            [cam_3_X, EoL_4_Y]  ...   \n",
       "50%                    [EoL_6_Y, EoL_3_X, EoL_2_Y]  ...   \n",
       "20%                             [EoL_3_X, EoL_3_Y]  ...   \n",
       "\n",
       "                                  gripper_1-7.8_55  \\\n",
       "abnormal Size Percent                                \n",
       "200%                   [EoL_5_X, EoL_2_X, EoL_4_Y]   \n",
       "150%                            [EoL_3_Y, EoL_5_Y]   \n",
       "100%                   [EoL_2_X, EoL_6_X, EoL_5_X]   \n",
       "50%                                      [EoL_5_X]   \n",
       "20%                             [EoL_5_Y, EoL_2_Y]   \n",
       "\n",
       "                                  max_Vel_2-7.9_56  \\\n",
       "abnormal Size Percent                                \n",
       "200%                                [rob_2_maxVel]   \n",
       "150%                       [EoL_6_Y, rob_2_maxVel]   \n",
       "100%                            [EoL_6_Y, EoL_6_X]   \n",
       "50%                        [rob_2_maxVel, EoL_6_Y]   \n",
       "20%                    [EoL_4_Y, EoL_6_X, EoL_6_Y]   \n",
       "\n",
       "                                     size_1-8.2_57  \\\n",
       "abnormal Size Percent                                \n",
       "200%                   [EoL_3_X, EoL_4_X, cam_1_X]   \n",
       "150%                            [EoL_3_Y, EoL_6_Y]   \n",
       "100%                            [EoL_5_Y, cam_1_Y]   \n",
       "50%                    [EoL_3_X, EoL_4_X, EoL_3_Y]   \n",
       "20%                             [cam_1_Y, EoL_4_Y]   \n",
       "\n",
       "                                   feeder_3-8.3_58  \\\n",
       "abnormal Size Percent                                \n",
       "200%                                     [EoL_6_X]   \n",
       "150%                            [EoL_5_X, cam_3_X]   \n",
       "100%                            [EoL_6_X, cam_3_Y]   \n",
       "50%                    [EoL_5_Y, EoL_6_Y, EoL_2_X]   \n",
       "20%                    [EoL_4_Y, EoL_5_Y, EoL_2_X]   \n",
       "\n",
       "                                  gripper_1-8.4_59         max_Vel_2-8.5_60  \\\n",
       "abnormal Size Percent                                                         \n",
       "200%                   [EoL_3_X, EoL_5_Y, EoL_6_Y]                [EoL_6_X]   \n",
       "150%                   [EoL_4_X, EoL_2_Y, EoL_6_X]  [rob_2_maxVel, EoL_6_X]   \n",
       "100%                            [EoL_4_X, EoL_4_Y]           [rob_2_maxVel]   \n",
       "50%                    [EoL_6_X, EoL_5_X, EoL_3_Y]       [cam_2_X, EoL_6_Y]   \n",
       "20%                             [EoL_2_Y, EoL_4_Y]  [rob_2_maxVel, EoL_6_X]   \n",
       "\n",
       "                                     size_1-8.6_61  \\\n",
       "abnormal Size Percent                                \n",
       "200%                            [EoL_5_X, EoL_4_X]   \n",
       "150%                   [cam_1_Y, EoL_6_Y, EoL_5_X]   \n",
       "100%                            [EoL_3_Y, cam_1_Y]   \n",
       "50%                             [EoL_3_Y, EoL_6_Y]   \n",
       "20%                             [cam_1_X, EoL_3_X]   \n",
       "\n",
       "                                   feeder_3-8.7_62  \\\n",
       "abnormal Size Percent                                \n",
       "200%                            [EoL_4_X, EoL_5_Y]   \n",
       "150%                   [cam_3_X, EoL_4_Y, EoL_2_X]   \n",
       "100%                   [EoL_6_Y, EoL_3_X, EoL_5_X]   \n",
       "50%                    [EoL_5_Y, cam_3_Y, EoL_2_X]   \n",
       "20%                             [EoL_5_Y, EoL_2_Y]   \n",
       "\n",
       "                                       gripper_1-8.8_63  \\\n",
       "abnormal Size Percent                                     \n",
       "200%                                 [EoL_3_Y, EoL_3_X]   \n",
       "150%                                 [EoL_4_X, EoL_3_X]   \n",
       "100%                                 [EoL_4_X, EoL_6_X]   \n",
       "50%                    [EoL_3_X, EoL_2_X, rob_1_supply]   \n",
       "20%                                  [EoL_3_X, EoL_6_Y]   \n",
       "\n",
       "                                  max_Vel_2-8.9_64  \n",
       "abnormal Size Percent                               \n",
       "200%                       [EoL_6_Y, rob_2_maxVel]  \n",
       "150%                            [EoL_6_Y, EoL_6_X]  \n",
       "100%                                [rob_2_maxVel]  \n",
       "50%                             [EoL_6_Y, EoL_6_X]  \n",
       "20%                    [EoL_6_Y, EoL_6_X, cam_2_X]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_abnormal_size_RCD = pd.DataFrame()\n",
    "result_abnormal_size_RCD = pd.concat([result_RCD_abnormal_size_1,result_RCD_abnormal_size_2,result_RCD_abnormal_size_3,result_RCD_abnormal_size_4,result_RCD_abnormal_size_5], ignore_index=True)\n",
    "result_abnormal_size_RCD['abnormal Size Percent'] = pd.DataFrame({'RCD':['200%','150%','100%','50%','20%']})\n",
    "result_abnormal_size_RCD.set_index('abnormal Size Percent', inplace=True)\n",
    "result_abnormal_size_RCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "df026c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.28125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%      150%      100%    50%      20%\n",
       "size_1-8.6_61     0.000000  1.000000  1.000000  0.000  1.00000\n",
       "feeder_3-8.7_62   0.000000  1.000000  0.000000  1.000  0.00000\n",
       "gripper_1-8.8_63  0.000000  0.000000  0.000000  1.000  0.00000\n",
       "max_Vel_2-8.9_64  1.000000  0.000000  1.000000  0.000  0.00000\n",
       "Total             0.453125  0.453125  0.453125  0.375  0.28125"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_abnormal_size_RCD,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c4408e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.171875</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>0.1875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%      150%     100%       50%     20%\n",
       "size_1-8.6_61     0.000000  1.000000  0.00000  0.000000  1.0000\n",
       "feeder_3-8.7_62   0.000000  1.000000  0.00000  0.000000  0.0000\n",
       "gripper_1-8.8_63  0.000000  0.000000  0.00000  0.000000  0.0000\n",
       "max_Vel_2-8.9_64  0.000000  0.000000  1.00000  0.000000  0.0000\n",
       "Total             0.265625  0.171875  0.15625  0.203125  0.1875"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_abnormal_size_RCD,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "faff060b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_3_RCD.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_3_RCD.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a6ece9",
   "metadata": {},
   "source": [
    "## Variation 4 - Causal Graph, adjacency matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c38cd1",
   "metadata": {},
   "source": [
    "Only the HT- and RW-Algorithm use an adjacency matrix for training of each model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d885080b",
   "metadata": {},
   "source": [
    "### HT - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "8077d1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_HT_edges_1 = run_HT_edges_delete(folder_path,files,startrow=839,edges_delete_n=20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_edges_2 = run_HT_edges_delete(folder_path,files,startrow=839,edges_delete_n=40,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_edges_3 = run_HT_edges_delete(folder_path,files,startrow=839,edges_delete_n=60,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_edges_4 = run_HT_edges_delete(folder_path,files,startrow=839,edges_delete_n=80,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_edges_5 = run_HT_edges_delete(folder_path,files,startrow=839,edges_delete_n=100,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e3982684",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edges Missing</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_3]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_1_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, cam_3_X, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_3]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_3]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, EoL_5_X]</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, EoL_5_X]</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, EoL_5_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, EoL_5_X]</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, rob_1_1]</td>\n",
       "      <td>[EoL_4_Y, rob_1_maxVel, EoL_5_X]</td>\n",
       "      <td>[rob_1_supply, EoL_4_Y, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>[rob_1_1, EoL_4_Y, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, cam_3_Y, score]</td>\n",
       "      <td>[rob_1_supply, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[rob_1_1, EoL_4_Y, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, cam_3_Y, score]</td>\n",
       "      <td>[rob_1_supply, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[rob_1_1, EoL_4_Y, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, cam_3_Y, score]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[rob_1_1, EoL_4_Y, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, cam_3_Y, score]</td>\n",
       "      <td>[rob_1_supply, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "      <td>[rob_1_1, EoL_4_Y, rob_1_4]</td>\n",
       "      <td>[EoL_2_Y, cam_3_Y, score]</td>\n",
       "      <td>[rob_1_supply, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_1, EoL_6_X]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   size_1-1.2_1  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "40             [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "60             [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "80             [EoL_4_Y, rob_1_maxVel, rob_1_1]   \n",
       "100                 [rob_1_1, EoL_4_Y, rob_1_4]   \n",
       "\n",
       "                                      feeder_3-1.3_2  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, cam_3_X, rob_1_maxVel]   \n",
       "40             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "80                  [EoL_4_Y, rob_1_maxVel, EoL_5_X]   \n",
       "100                        [EoL_2_Y, cam_3_Y, score]   \n",
       "\n",
       "                                          gripper_1-1.4_3  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "40             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "60             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "80                  [rob_1_supply, EoL_4_Y, rob_1_maxVel]   \n",
       "100                      [rob_1_supply, rob_1_1, rob_1_4]   \n",
       "\n",
       "                                          max_Vel_2-1.5_4  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "40                  [rob_2_maxVel, rob_1_maxVel, rob_1_1]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "80                       [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "100                      [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "\n",
       "                                   size_1-1.6_5  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "40             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "60             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "80             [EoL_4_Y, rob_1_maxVel, rob_1_1]   \n",
       "100                 [rob_1_1, EoL_4_Y, rob_1_4]   \n",
       "\n",
       "                                      feeder_3-1.7_6  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, cam_3_X, rob_1_maxVel]   \n",
       "40             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "80                  [EoL_4_Y, rob_1_maxVel, EoL_5_X]   \n",
       "100                        [EoL_2_Y, cam_3_Y, score]   \n",
       "\n",
       "                                          gripper_1-1.8_7  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "40             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "60             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "80                  [rob_1_supply, EoL_4_Y, rob_1_maxVel]   \n",
       "100                      [rob_1_supply, rob_1_1, rob_1_4]   \n",
       "\n",
       "                                          max_Vel_2-1.9_8  \\\n",
       "Edges Missing                                               \n",
       "20                  [rob_2_maxVel, rob_1_maxVel, rob_1_3]   \n",
       "40                  [rob_2_maxVel, rob_1_maxVel, rob_1_3]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "80                       [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "100                      [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "\n",
       "                                   size_1-2.2_9  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "40             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "60             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "80             [EoL_4_Y, rob_1_maxVel, rob_1_1]   \n",
       "100                 [rob_1_1, EoL_4_Y, rob_1_4]   \n",
       "\n",
       "                                     feeder_3-2.3_10  ...  \\\n",
       "Edges Missing                                         ...   \n",
       "20             [rob_2_maxVel, cam_3_X, rob_1_maxVel]  ...   \n",
       "40             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "80                  [EoL_4_Y, rob_1_maxVel, EoL_5_X]  ...   \n",
       "100                        [EoL_2_Y, cam_3_Y, score]  ...   \n",
       "\n",
       "                                         gripper_1-7.8_55  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "40             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "60             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "80                  [rob_1_supply, EoL_4_Y, rob_1_maxVel]   \n",
       "100                      [rob_1_supply, rob_1_1, rob_1_4]   \n",
       "\n",
       "                                         max_Vel_2-7.9_56  \\\n",
       "Edges Missing                                               \n",
       "20                  [rob_2_maxVel, rob_1_maxVel, rob_1_1]   \n",
       "40                  [rob_2_maxVel, rob_1_maxVel, rob_1_1]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "80                       [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "100                      [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "\n",
       "                                  size_1-8.2_57  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "40             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "60             [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "80             [EoL_4_Y, rob_1_maxVel, rob_1_1]   \n",
       "100                 [rob_1_1, EoL_4_Y, rob_1_4]   \n",
       "\n",
       "                                     feeder_3-8.3_58  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, cam_3_X, rob_1_maxVel]   \n",
       "40             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "80                  [EoL_4_Y, rob_1_maxVel, EoL_5_X]   \n",
       "100                        [EoL_2_Y, cam_3_Y, score]   \n",
       "\n",
       "                                         gripper_1-8.4_59  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "40             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "60             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "80                  [rob_1_supply, EoL_4_Y, rob_1_maxVel]   \n",
       "100                      [rob_1_supply, rob_1_1, rob_1_4]   \n",
       "\n",
       "                                         max_Vel_2-8.5_60  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "40                  [rob_2_maxVel, rob_1_maxVel, rob_1_3]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "80                       [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "100                      [rob_2_maxVel, rob_1_1, EoL_6_X]   \n",
       "\n",
       "                                  size_1-8.6_61  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "40             [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "60             [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "80             [EoL_4_Y, rob_1_maxVel, rob_1_1]   \n",
       "100                 [rob_1_1, EoL_4_Y, rob_1_4]   \n",
       "\n",
       "                                     feeder_3-8.7_62  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, cam_3_X, rob_1_maxVel]   \n",
       "40             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "60             [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "80                  [EoL_4_Y, rob_1_maxVel, EoL_5_X]   \n",
       "100                        [EoL_2_Y, cam_3_Y, score]   \n",
       "\n",
       "                                         gripper_1-8.8_63  \\\n",
       "Edges Missing                                               \n",
       "20             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "40             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "60             [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "80                  [rob_1_supply, EoL_4_Y, rob_1_maxVel]   \n",
       "100                      [rob_1_supply, rob_1_1, rob_1_4]   \n",
       "\n",
       "                                         max_Vel_2-8.9_64  \n",
       "Edges Missing                                              \n",
       "20                  [rob_2_maxVel, rob_1_maxVel, rob_1_3]  \n",
       "40                  [rob_2_maxVel, rob_1_maxVel, rob_1_3]  \n",
       "60             [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "80                       [rob_2_maxVel, rob_1_1, EoL_6_X]  \n",
       "100                      [rob_2_maxVel, rob_1_1, EoL_6_X]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_edges_HT = pd.DataFrame()\n",
    "result_edges_HT = pd.concat([result_HT_edges_1,result_HT_edges_2,result_HT_edges_3,result_HT_edges_4,result_HT_edges_5], ignore_index=True)\n",
    "result_edges_HT['Edges Missing'] = pd.DataFrame({'HT':['20','40','60','80','100']})\n",
    "result_edges_HT.set_index('Edges Missing', inplace=True)\n",
    "result_edges_HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9db1f5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.734375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    20      40   60   80       100\n",
       "size_1-8.6_61     0.00  0.0000  0.0  0.0  0.000000\n",
       "feeder_3-8.7_62   1.00  0.0000  0.0  0.0  1.000000\n",
       "gripper_1-8.8_63  1.00  1.0000  1.0  1.0  1.000000\n",
       "max_Vel_2-8.9_64  1.00  1.0000  1.0  1.0  1.000000\n",
       "Total             0.75  0.5625  0.5  0.5  0.734375"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_edges_HT,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c737c41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.484375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       20   40   60   80       100\n",
       "size_1-8.6_61     0.00000  0.0  0.0  0.0  0.000000\n",
       "feeder_3-8.7_62   0.00000  0.0  0.0  0.0  0.000000\n",
       "gripper_1-8.8_63  1.00000  1.0  1.0  1.0  1.000000\n",
       "max_Vel_2-8.9_64  1.00000  1.0  1.0  1.0  1.000000\n",
       "Total             0.59375  0.5  0.5  0.5  0.484375"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_edges_HT,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "764786b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_4_HT.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_4_HT.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14cfa42b",
   "metadata": {},
   "source": [
    "### RW - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d07a876f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW_edges_1 = run_RW_edges_delete(folder_path,files,startrow=839,edges_delete_n=20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_edges_2 = run_RW_edges_delete(folder_path,files,startrow=839,edges_delete_n=40,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_edges_3 = run_RW_edges_delete(folder_path,files,startrow=839,edges_delete_n=60,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_edges_4 = run_RW_edges_delete(folder_path,files,startrow=839,edges_delete_n=80,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_edges_5 = run_RW_edges_delete(folder_path,files,startrow=839,edges_delete_n=100,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8912e5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edges Missing</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[rob_2_supply, con_2, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, con_2, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, con_2, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, con_2]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, con_2, cam_1_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_2_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_2_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_2_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_2_X]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_vacuum, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_2_maxVel, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>[con_2, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_2_X, cam_3_X]</td>\n",
       "      <td>[cam_1_Y, con_2, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, con_2, cam_1_Y]</td>\n",
       "      <td>[con_2, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[con_2, cam_1_X, cam_2_X]</td>\n",
       "      <td>[con_2, cam_1_X, cam_2_X]</td>\n",
       "      <td>[rob_2_maxVel, con_1, con_2]</td>\n",
       "      <td>[cam_1_Y, con_2, cam_1_X]</td>\n",
       "      <td>[con_2, cam_1_Y, cam_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_1_X, cam_2_X, cam_3_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, con_2]</td>\n",
       "      <td>[con_2, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[con_2, cam_1_X, cam_2_X]</td>\n",
       "      <td>[con_2, rob_2_vacuum, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, con_2]</td>\n",
       "      <td>[con_2, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, con_2, cam_1_X]</td>\n",
       "      <td>[con_2, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, con_2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, cam_1_X]</td>\n",
       "      <td>[EoL_1_X, rob_2_vacuum, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[EoL_1_X, rob_2_vacuum, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[EoL_1_X, rob_2_vacuum, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[rob_2_vacuum, rob_1_vacuum, EoL_1_X]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, rob_1_vacuum]</td>\n",
       "      <td>[rob_2_vacuum, EoL_1_X, rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_1_X, EoL_5_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_1_X, EoL_5_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_1_X, EoL_5_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_1_X, cam_1_X]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   size_1-1.2_1  \\\n",
       "Edges Missing                                     \n",
       "20               [rob_2_supply, con_2, cam_1_X]   \n",
       "40             [rob_2_supply, cam_1_X, cam_2_X]   \n",
       "60                    [con_2, cam_1_Y, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, cam_1_X]   \n",
       "100                 [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                      feeder_3-1.3_2  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                       [cam_1_X, cam_2_X, cam_3_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     gripper_1-1.4_3  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                         [cam_1_Y, con_2, cam_1_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, cam_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     max_Vel_2-1.5_4  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "40             [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "60                    [rob_2_maxVel, con_2, cam_1_Y]   \n",
       "80             [EoL_1_X, rob_2_vacuum, rob_2_maxVel]   \n",
       "100                      [EoL_1_X, EoL_5_X, cam_1_X]   \n",
       "\n",
       "                                   size_1-1.6_5  \\\n",
       "Edges Missing                                     \n",
       "20               [rob_2_supply, con_2, cam_1_X]   \n",
       "40             [rob_2_supply, cam_1_X, cam_2_X]   \n",
       "60                    [con_2, cam_1_Y, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, cam_1_X]   \n",
       "100                 [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                      feeder_3-1.7_6  \\\n",
       "Edges Missing                                          \n",
       "20               [rob_1_vacuum, rob_2_supply, con_2]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                         [con_2, cam_1_X, cam_2_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     gripper_1-1.8_7  \\\n",
       "Edges Missing                                          \n",
       "20               [rob_1_vacuum, rob_2_supply, con_2]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                         [con_2, cam_1_X, cam_2_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     max_Vel_2-1.9_8  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, cam_1_X, rob_2_supply]   \n",
       "40             [rob_2_maxVel, cam_1_X, rob_2_supply]   \n",
       "60                      [rob_2_maxVel, con_1, con_2]   \n",
       "80             [EoL_1_X, rob_2_vacuum, rob_2_maxVel]   \n",
       "100                      [EoL_1_X, EoL_5_X, cam_1_X]   \n",
       "\n",
       "                                   size_1-2.2_9  \\\n",
       "Edges Missing                                     \n",
       "20               [rob_2_supply, con_2, cam_1_X]   \n",
       "40             [rob_2_supply, cam_1_X, cam_2_X]   \n",
       "60                    [cam_1_Y, con_2, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, cam_1_X]   \n",
       "100                 [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     feeder_3-2.3_10  ...  \\\n",
       "Edges Missing                                         ...   \n",
       "20               [rob_1_vacuum, rob_2_supply, con_2]  ...   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_3_Y]  ...   \n",
       "60                         [con_2, cam_1_Y, cam_1_X]  ...   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]  ...   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]  ...   \n",
       "\n",
       "                                    gripper_1-7.8_55  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                       [cam_1_X, cam_2_X, cam_3_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                    max_Vel_2-7.9_56  \\\n",
       "Edges Missing                                          \n",
       "20               [rob_2_supply, rob_2_maxVel, con_2]   \n",
       "40             [rob_2_supply, rob_2_maxVel, cam_1_X]   \n",
       "60                    [rob_2_maxVel, cam_1_Y, con_2]   \n",
       "80             [EoL_1_X, rob_2_vacuum, rob_2_maxVel]   \n",
       "100                      [EoL_1_X, EoL_5_X, cam_1_X]   \n",
       "\n",
       "                                  size_1-8.2_57  \\\n",
       "Edges Missing                                     \n",
       "20             [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "40             [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "60                    [con_2, cam_1_Y, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, cam_1_X]   \n",
       "100                 [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     feeder_3-8.3_58  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_1_vacuum, rob_2_supply, cam_3_Y]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_Y]   \n",
       "60                         [con_2, cam_1_X, cam_2_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                    gripper_1-8.4_59  \\\n",
       "Edges Missing                                          \n",
       "20               [rob_1_vacuum, rob_2_supply, con_2]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_Y]   \n",
       "60                    [con_2, rob_2_vacuum, cam_1_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                    max_Vel_2-8.5_60  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "40             [rob_2_supply, rob_2_maxVel, cam_1_Y]   \n",
       "60                    [rob_2_maxVel, cam_1_Y, con_2]   \n",
       "80             [rob_2_vacuum, EoL_1_X, rob_2_maxVel]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                  size_1-8.6_61  \\\n",
       "Edges Missing                                     \n",
       "20               [rob_2_supply, con_2, cam_1_Y]   \n",
       "40             [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "60               [con_2, rob_2_maxVel, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, cam_1_X]   \n",
       "100                 [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                     feeder_3-8.7_62  \\\n",
       "Edges Missing                                          \n",
       "20             [rob_1_vacuum, rob_2_supply, cam_2_Y]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_3_Y]   \n",
       "60                    [rob_2_vacuum, con_2, cam_1_X]   \n",
       "80             [rob_2_vacuum, rob_1_vacuum, EoL_1_X]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                    gripper_1-8.8_63  \\\n",
       "Edges Missing                                          \n",
       "20               [rob_1_vacuum, rob_2_supply, con_2]   \n",
       "40             [rob_1_vacuum, rob_2_supply, cam_1_X]   \n",
       "60                         [con_2, cam_1_Y, cam_1_X]   \n",
       "80             [rob_2_vacuum, EoL_1_X, rob_1_vacuum]   \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]   \n",
       "\n",
       "                                    max_Vel_2-8.9_64  \n",
       "Edges Missing                                         \n",
       "20             [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "40             [rob_2_supply, rob_2_maxVel, cam_1_Y]  \n",
       "60                    [rob_2_maxVel, cam_1_Y, con_2]  \n",
       "80             [rob_2_vacuum, EoL_1_X, rob_2_maxVel]  \n",
       "100                      [EoL_5_X, EoL_1_X, cam_1_X]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_edges_RW = pd.DataFrame()\n",
    "result_edges_RW = pd.concat([result_RW_edges_1,result_RW_edges_2,result_RW_edges_3,result_RW_edges_4,result_RW_edges_5], ignore_index=True)\n",
    "result_edges_RW['Edges Missing'] = pd.DataFrame({'RW':['20','40','60','80','100']})\n",
    "result_edges_RW.set_index('Edges Missing', inplace=True)\n",
    "result_edges_RW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "36cd3c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.53125</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       20       40       60   80   100\n",
       "size_1-8.6_61     1.00000  1.00000  1.00000  1.0  1.00\n",
       "feeder_3-8.7_62   0.00000  1.00000  0.00000  0.0  0.00\n",
       "gripper_1-8.8_63  0.00000  0.00000  0.00000  0.0  0.00\n",
       "max_Vel_2-8.9_64  1.00000  1.00000  1.00000  1.0  0.00\n",
       "Total             0.59375  0.59375  0.53125  0.5  0.25"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_edges_RW,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ad63b68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>20</th>\n",
       "      <th>40</th>\n",
       "      <th>60</th>\n",
       "      <th>80</th>\n",
       "      <th>100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.09375</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      20       40    60   80  100\n",
       "size_1-8.6_61     0.0000  0.00000  0.00  0.0  0.0\n",
       "feeder_3-8.7_62   0.0000  0.00000  0.00  0.0  0.0\n",
       "gripper_1-8.8_63  0.0000  0.00000  0.00  0.0  0.0\n",
       "max_Vel_2-8.9_64  1.0000  0.00000  1.00  0.0  0.0\n",
       "Total             0.1875  0.09375  0.25  0.0  0.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_edges_RW,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "40b0dec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_4_RW.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_4_RW.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f04deb0",
   "metadata": {},
   "source": [
    "## Variation 5 - Size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053b2226",
   "metadata": {},
   "source": [
    "### HT -Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "566d859a",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_HT_size_1 = run_HT_size(folder_path,files,startrow=839,size_p=2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_size_2 = run_HT_size(folder_path,files,startrow=839,size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_size_3 = run_HT_size(folder_path,files,startrow=839,size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_size_4 = run_HT_size(folder_path,files,startrow=839,size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_HT_size_5 = run_HT_size(folder_path,files,startrow=839,size_p=0.2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a8f6e28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, EoL_4_Y]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[con_3, con_2, rob_1_maxVel]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, con_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "      <td>[rob_1_maxVel, con_3, con_2]</td>\n",
       "      <td>[cam_3_X, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_maxVel, con_3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[rob_1_1, rob_1_2, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_1, rob_1_2, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_2]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_4]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[rob_1_maxVel, rob_1_1, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_1, rob_1_4]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_1_3]</td>\n",
       "      <td>[rob_1_3, rob_1_1, rob_1_2]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_1_3]</td>\n",
       "      <td>[rob_1_3, rob_1_1, rob_1_2]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_1_3]</td>\n",
       "      <td>[rob_1_1, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_1_3]</td>\n",
       "      <td>[rob_1_3, rob_1_1, rob_1_2]</td>\n",
       "      <td>[cam_3_X, EoL_2_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_1_1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_1]</td>\n",
       "      <td>[score, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_2_2]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_1]</td>\n",
       "      <td>[score, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_1_vacuum, rob_2_1]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_3]</td>\n",
       "      <td>[score, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_2_2]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_3]</td>\n",
       "      <td>[score, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_3]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_2_2]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_1_3]</td>\n",
       "      <td>[score, rob_2_maxVel, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_1_vacuum, rob_1_1]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_1, rob_2_2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               size_1-1.2_1  \\\n",
       "Data Size                                     \n",
       "200%       [rob_1_maxVel, rob_1_3, rob_1_1]   \n",
       "150%           [con_3, con_2, rob_1_maxVel]   \n",
       "100%            [rob_1_1, rob_1_2, rob_1_3]   \n",
       "50%             [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%             [cam_1_X, cam_1_Y, rob_1_1]   \n",
       "\n",
       "                                  feeder_3-1.3_2  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%       [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                 [rob_1_2, rob_1_1, rob_1_4]   \n",
       "50%             [cam_3_X, EoL_2_X, rob_2_maxVel]   \n",
       "20%          [score, rob_2_maxVel, rob_1_maxVel]   \n",
       "\n",
       "                                      gripper_1-1.4_3  \\\n",
       "Data Size                                               \n",
       "200%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%            [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "50%             [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "20%             [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "\n",
       "                                      max_Vel_2-1.5_4  \\\n",
       "Data Size                                               \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%              [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%            [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                  [rob_2_maxVel, rob_2_1, rob_1_3]   \n",
       "20%                  [rob_2_maxVel, rob_2_1, rob_2_2]   \n",
       "\n",
       "                               size_1-1.6_5  \\\n",
       "Data Size                                     \n",
       "200%       [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%           [rob_1_maxVel, con_3, con_2]   \n",
       "100%            [rob_1_1, rob_1_2, rob_1_3]   \n",
       "50%             [rob_1_3, rob_1_1, rob_1_2]   \n",
       "20%             [cam_1_X, cam_1_Y, rob_1_1]   \n",
       "\n",
       "                                  feeder_3-1.7_6  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%       [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                 [rob_1_2, rob_1_1, rob_1_4]   \n",
       "50%             [cam_3_X, EoL_2_X, rob_2_maxVel]   \n",
       "20%          [score, rob_2_maxVel, rob_1_maxVel]   \n",
       "\n",
       "                                      gripper_1-1.8_7  \\\n",
       "Data Size                                               \n",
       "200%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%             [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "20%             [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                      max_Vel_2-1.9_8  \\\n",
       "Data Size                                               \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%              [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%            [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                  [rob_2_maxVel, rob_2_1, rob_1_3]   \n",
       "20%             [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "\n",
       "                               size_1-2.2_9  \\\n",
       "Data Size                                     \n",
       "200%       [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%           [rob_1_maxVel, con_3, con_2]   \n",
       "100%       [rob_1_maxVel, rob_1_1, rob_1_2]   \n",
       "50%             [rob_1_3, rob_1_1, rob_1_2]   \n",
       "20%             [cam_1_X, cam_1_Y, rob_1_3]   \n",
       "\n",
       "                                 feeder_3-2.3_10  ...  \\\n",
       "Data Size                                         ...   \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]  ...   \n",
       "150%       [cam_3_X, rob_2_maxVel, rob_1_maxVel]  ...   \n",
       "100%                 [rob_1_2, rob_1_1, rob_1_4]  ...   \n",
       "50%             [cam_3_X, EoL_2_X, rob_2_maxVel]  ...   \n",
       "20%          [score, rob_2_maxVel, rob_1_maxVel]  ...   \n",
       "\n",
       "                                     gripper_1-7.8_55  \\\n",
       "Data Size                                               \n",
       "200%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%             [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "20%             [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                     max_Vel_2-7.9_56  \\\n",
       "Data Size                                               \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%              [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%            [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                  [rob_2_maxVel, rob_2_1, rob_1_3]   \n",
       "20%                  [rob_2_maxVel, rob_2_1, rob_2_2]   \n",
       "\n",
       "                              size_1-8.2_57  \\\n",
       "Data Size                                     \n",
       "200%       [rob_1_maxVel, rob_1_3, rob_1_2]   \n",
       "150%           [rob_1_maxVel, con_3, con_2]   \n",
       "100%            [rob_1_2, rob_1_1, rob_1_3]   \n",
       "50%             [rob_1_1, rob_1_3, rob_1_2]   \n",
       "20%             [cam_1_X, cam_1_Y, rob_1_3]   \n",
       "\n",
       "                                 feeder_3-8.3_58  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%       [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                 [rob_1_2, rob_1_1, rob_1_4]   \n",
       "50%             [cam_3_X, EoL_2_X, rob_2_maxVel]   \n",
       "20%          [score, rob_2_maxVel, rob_1_maxVel]   \n",
       "\n",
       "                                gripper_1-8.4_59  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "150%         [rob_1_supply, rob_1_vacuum, con_3]   \n",
       "100%       [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "50%        [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%        [rob_1_supply, rob_1_vacuum, rob_1_3]   \n",
       "\n",
       "                                     max_Vel_2-8.5_60  \\\n",
       "Data Size                                               \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]   \n",
       "150%              [rob_2_maxVel, rob_1_maxVel, con_3]   \n",
       "100%            [rob_2_maxVel, rob_1_vacuum, rob_2_1]   \n",
       "50%                  [rob_2_maxVel, rob_2_1, rob_1_3]   \n",
       "20%                  [rob_2_maxVel, rob_2_1, rob_2_2]   \n",
       "\n",
       "                              size_1-8.6_61  \\\n",
       "Data Size                                     \n",
       "200%       [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "150%           [rob_1_maxVel, con_3, con_2]   \n",
       "100%       [rob_1_maxVel, rob_1_1, rob_1_3]   \n",
       "50%             [rob_1_3, rob_1_1, rob_1_2]   \n",
       "20%             [cam_1_X, cam_1_Y, rob_1_3]   \n",
       "\n",
       "                                 feeder_3-8.7_62  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, EoL_4_Y]   \n",
       "150%       [cam_3_X, rob_2_maxVel, rob_1_maxVel]   \n",
       "100%                 [rob_1_2, rob_1_1, rob_1_4]   \n",
       "50%             [cam_3_X, EoL_2_X, rob_2_maxVel]   \n",
       "20%          [score, rob_2_maxVel, rob_1_maxVel]   \n",
       "\n",
       "                                     gripper_1-8.8_63  \\\n",
       "Data Size                                               \n",
       "200%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "150%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "100%       [rob_1_supply, rob_1_vacuum, rob_1_maxVel]   \n",
       "50%             [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "20%             [rob_1_supply, rob_1_vacuum, rob_1_1]   \n",
       "\n",
       "                                     max_Vel_2-8.9_64  \n",
       "Data Size                                              \n",
       "200%       [rob_2_maxVel, rob_1_maxVel, rob_1_vacuum]  \n",
       "150%              [rob_2_maxVel, rob_1_maxVel, con_3]  \n",
       "100%            [rob_2_maxVel, rob_1_vacuum, rob_2_1]  \n",
       "50%                  [rob_2_maxVel, rob_2_1, rob_1_1]  \n",
       "20%                  [rob_2_maxVel, rob_2_1, rob_2_2]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_size_HT = pd.DataFrame()\n",
    "result_size_HT = pd.concat([result_HT_size_1,result_HT_size_2,result_HT_size_3,result_HT_size_4,result_HT_size_5], ignore_index=True)\n",
    "result_size_HT['Data Size'] = pd.DataFrame({'HT':['200%','150%','100%','50%','20%']})\n",
    "result_size_HT.set_index('Data Size', inplace=True)\n",
    "result_size_HT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "eac14219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      200%  150%  100%   50%   20%\n",
       "size_1-8.6_61     0.000000  0.00   0.0  0.00  1.00\n",
       "feeder_3-8.7_62   0.000000  1.00   0.0  1.00  0.00\n",
       "gripper_1-8.8_63  1.000000  1.00   1.0  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.000000  1.00   1.0  1.00  1.00\n",
       "Total             0.578125  0.75   0.5  0.75  0.75"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_size_HT,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "731942f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%  150%  100%   50%   20%\n",
       "size_1-8.6_61      0.0  0.00   0.0  0.00  1.00\n",
       "feeder_3-8.7_62    0.0  1.00   0.0  1.00  0.00\n",
       "gripper_1-8.8_63   1.0  1.00   1.0  1.00  1.00\n",
       "max_Vel_2-8.9_64   1.0  1.00   1.0  1.00  1.00\n",
       "Total              0.5  0.75   0.5  0.75  0.75"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_size_HT,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f92ac432",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_5_HT.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_5_HT.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8055b8ec",
   "metadata": {},
   "source": [
    "### ED - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6284bad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ED_size_1 = run_ED_size(folder_path,files,startrow=839,size_p=2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_size_2 = run_ED_size(folder_path,files,startrow=839,size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_size_3 = run_ED_size(folder_path,files,startrow=839,size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_size_4 = run_ED_size(folder_path,files,startrow=839,size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_ED_size_5 = run_ED_size(folder_path,files,startrow=839,size_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "94ec65c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, con_1]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_Y, con_2, con_3]</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_X, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, EoL_1_X, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, con_3, cam_2_Y]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_X, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, con_2, cam_2_Y]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, con_1]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, cam_1_Y, cam_2_Y]</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_X, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, EoL_1_X, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, con_3, con_1]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_X, cam_3_Y, rob_1_vacuum]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, con_2]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[EoL_3_Y, cam_3_Y, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[EoL_1_X, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, con_2]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, cam_1_Y]</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[EoL_1_X, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_3_Y, cam_3_X, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, EoL_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, rob_1_vacuum, rob_1_4]</td>\n",
       "      <td>[EoL_1_Y, con_3, con_2]</td>\n",
       "      <td>[cam_3_Y, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_3_Y, EoL_1_X, rob_1_vacuum]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, con_2]</td>\n",
       "      <td>[cam_3_Y, EoL_1_X, rob_1_4]</td>\n",
       "      <td>[cam_3_Y, EoL_3_Y, rob_1_vacuum]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, con_1]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[EoL_3_X, EoL_4_X, EoL_5_X]</td>\n",
       "      <td>[cam_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, cam_1_Y]</td>\n",
       "      <td>[cam_3_Y, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[cam_3_Y, EoL_3_X, EoL_4_X]</td>\n",
       "      <td>[cam_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, con_2]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_Y, cam_2_Y, cam_3_X]</td>\n",
       "      <td>[EoL_2_X, EoL_3_X, EoL_4_X]</td>\n",
       "      <td>[cam_3_Y, EoL_1_Y, EoL_2_X]</td>\n",
       "      <td>[cam_2_Y, EoL_1_Y, rob_1_supply]</td>\n",
       "      <td>[cam_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_5_X, EoL_3_Y]</td>\n",
       "      <td>[cam_3_Y, cam_2_Y, EoL_1_Y]</td>\n",
       "      <td>[EoL_1_Y, cam_2_Y, con_2]</td>\n",
       "      <td>[cam_3_Y, EoL_1_Y, cam_2_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_3_X, EoL_3_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_1_Y, con_1, rob_1_maxVel]</td>\n",
       "      <td>[rob_1_2, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_4, rob_1_3]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, con_2]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_2, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_4, rob_1_3]</td>\n",
       "      <td>[con_2, con_3, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[con_2, con_3, con_1]</td>\n",
       "      <td>[rob_1_4, rob_1_3, rob_1_2]</td>\n",
       "      <td>[rob_1_vacuum, rob_1_4, rob_1_3]</td>\n",
       "      <td>[rob_1_2, rob_1_3, rob_1_4]</td>\n",
       "      <td>[con_2, cam_1_Y, con_1]</td>\n",
       "      <td>[rob_1_2, rob_1_3, rob_1_1]</td>\n",
       "      <td>[rob_1_3, rob_1_2, rob_1_1]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               size_1-1.2_1                  feeder_3-1.3_2  \\\n",
       "Data Size                                                                     \n",
       "200%       [rob_1_vacuum, rob_1_4, rob_1_3]       [cam_2_Y, EoL_1_Y, con_1]   \n",
       "150%       [rob_1_vacuum, rob_1_4, rob_1_3]       [cam_2_Y, EoL_1_Y, con_1]   \n",
       "100%       [EoL_1_X, rob_1_vacuum, rob_1_4]       [cam_2_Y, EoL_1_Y, con_2]   \n",
       "50%             [cam_3_Y, EoL_1_Y, cam_2_Y]       [EoL_1_Y, cam_2_Y, con_1]   \n",
       "20%        [rob_1_vacuum, rob_1_4, rob_1_3]  [cam_1_Y, con_1, rob_1_maxVel]   \n",
       "\n",
       "                       gripper_1-1.4_3                   max_Vel_2-1.5_4  \\\n",
       "Data Size                                                                  \n",
       "200%       [rob_1_4, rob_1_3, rob_1_2]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "150%       [rob_1_4, rob_1_3, rob_1_2]  [cam_3_Y, rob_1_vacuum, rob_1_4]   \n",
       "100%       [rob_1_4, rob_1_3, rob_1_2]  [cam_3_Y, rob_1_vacuum, rob_1_4]   \n",
       "50%        [EoL_1_Y, cam_2_Y, cam_3_X]       [EoL_3_X, EoL_4_X, EoL_5_X]   \n",
       "20%        [rob_1_2, rob_1_4, rob_1_3]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "\n",
       "                               size_1-1.6_5               feeder_3-1.7_6  \\\n",
       "Data Size                                                                  \n",
       "200%       [rob_1_vacuum, rob_1_4, rob_1_3]      [EoL_1_Y, con_2, con_3]   \n",
       "150%       [cam_3_Y, rob_1_vacuum, rob_1_4]  [EoL_1_Y, cam_1_Y, cam_2_Y]   \n",
       "100%            [cam_3_Y, rob_1_4, rob_1_3]  [EoL_1_Y, cam_2_Y, cam_1_Y]   \n",
       "50%             [cam_3_Y, EoL_1_Y, cam_2_Y]  [EoL_1_Y, cam_2_Y, cam_1_Y]   \n",
       "20%             [rob_1_2, rob_1_4, rob_1_3]    [cam_1_Y, cam_1_X, con_2]   \n",
       "\n",
       "                       gripper_1-1.8_7                   max_Vel_2-1.9_8  \\\n",
       "Data Size                                                                  \n",
       "200%       [cam_3_Y, rob_1_4, rob_1_3]  [EoL_1_X, rob_1_vacuum, rob_1_4]   \n",
       "150%       [cam_3_Y, rob_1_4, rob_1_3]  [EoL_1_X, rob_1_vacuum, rob_1_4]   \n",
       "100%       [cam_3_Y, rob_1_4, rob_1_3]       [EoL_1_X, rob_1_4, rob_1_3]   \n",
       "50%        [cam_3_Y, cam_2_Y, cam_3_X]       [cam_3_Y, EoL_3_X, EoL_4_X]   \n",
       "20%        [rob_1_4, rob_1_3, rob_1_2]       [rob_1_2, rob_1_4, rob_1_3]   \n",
       "\n",
       "                               size_1-2.2_9              feeder_3-2.3_10  ...  \\\n",
       "Data Size                                                                 ...   \n",
       "200%       [cam_3_Y, rob_1_vacuum, rob_1_4]    [EoL_1_Y, EoL_1_X, con_2]  ...   \n",
       "150%       [cam_3_Y, rob_1_vacuum, rob_1_4]    [EoL_1_Y, EoL_1_X, con_2]  ...   \n",
       "100%            [cam_3_Y, cam_3_X, rob_1_4]  [EoL_1_Y, cam_2_Y, EoL_1_X]  ...   \n",
       "50%             [cam_3_Y, EoL_1_Y, cam_2_Y]    [cam_2_Y, EoL_1_Y, con_2]  ...   \n",
       "20%             [rob_1_2, rob_1_4, rob_1_3]      [con_2, con_3, cam_1_Y]  ...   \n",
       "\n",
       "                      gripper_1-7.8_55                  max_Vel_2-7.9_56  \\\n",
       "Data Size                                                                  \n",
       "200%       [cam_3_Y, rob_1_4, rob_1_3]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "150%       [cam_3_Y, rob_1_4, rob_1_3]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "100%       [cam_3_Y, rob_1_4, rob_1_3]  [cam_3_Y, rob_1_vacuum, rob_1_4]   \n",
       "50%        [cam_3_Y, cam_2_Y, cam_3_X]       [EoL_2_X, EoL_3_X, EoL_4_X]   \n",
       "20%        [rob_1_4, rob_1_3, rob_1_2]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "\n",
       "                              size_1-8.2_57                   feeder_3-8.3_58  \\\n",
       "Data Size                                                                       \n",
       "200%       [cam_3_Y, rob_1_vacuum, rob_1_4]         [EoL_1_Y, con_3, cam_2_Y]   \n",
       "150%       [cam_3_Y, rob_1_vacuum, rob_1_4]           [EoL_1_Y, con_3, con_1]   \n",
       "100%       [cam_3_Y, rob_1_vacuum, rob_1_4]           [EoL_1_Y, con_3, con_2]   \n",
       "50%             [cam_3_Y, EoL_1_Y, EoL_2_X]  [cam_2_Y, EoL_1_Y, rob_1_supply]   \n",
       "20%        [rob_1_vacuum, rob_1_4, rob_1_3]             [con_2, con_3, con_1]   \n",
       "\n",
       "                      gripper_1-8.4_59                  max_Vel_2-8.5_60  \\\n",
       "Data Size                                                                  \n",
       "200%       [rob_1_4, rob_1_3, rob_1_2]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "150%       [rob_1_4, rob_1_3, rob_1_2]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "100%       [cam_3_Y, rob_1_4, rob_1_3]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "50%        [cam_3_Y, EoL_1_Y, cam_2_Y]       [EoL_3_X, EoL_5_X, EoL_3_Y]   \n",
       "20%        [rob_1_4, rob_1_3, rob_1_2]  [rob_1_vacuum, rob_1_4, rob_1_3]   \n",
       "\n",
       "                              size_1-8.6_61            feeder_3-8.7_62  \\\n",
       "Data Size                                                                \n",
       "200%       [EoL_1_X, rob_1_vacuum, rob_1_4]  [EoL_1_Y, con_2, cam_2_Y]   \n",
       "150%       [EoL_1_X, cam_3_Y, rob_1_vacuum]  [EoL_1_Y, cam_2_Y, con_2]   \n",
       "100%       [cam_3_Y, EoL_1_X, rob_1_vacuum]  [EoL_1_Y, cam_2_Y, con_2]   \n",
       "50%             [cam_3_Y, cam_2_Y, EoL_1_Y]  [EoL_1_Y, cam_2_Y, con_2]   \n",
       "20%             [rob_1_2, rob_1_3, rob_1_4]    [con_2, cam_1_Y, con_1]   \n",
       "\n",
       "                      gripper_1-8.8_63                  max_Vel_2-8.9_64  \n",
       "Data Size                                                                 \n",
       "200%       [rob_1_4, rob_1_3, rob_1_2]  [cam_3_Y, rob_1_vacuum, rob_1_4]  \n",
       "150%       [rob_1_4, rob_1_3, rob_1_2]  [EoL_3_Y, cam_3_Y, rob_1_vacuum]  \n",
       "100%       [cam_3_Y, EoL_1_X, rob_1_4]  [cam_3_Y, EoL_3_Y, rob_1_vacuum]  \n",
       "50%        [cam_3_Y, EoL_1_Y, cam_2_Y]       [EoL_2_X, EoL_3_X, EoL_3_Y]  \n",
       "20%        [rob_1_2, rob_1_3, rob_1_1]       [rob_1_3, rob_1_2, rob_1_1]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_size_ED = pd.DataFrame()\n",
    "result_size_ED = pd.concat([result_ED_size_1,result_ED_size_2,result_ED_size_3,result_ED_size_4,result_ED_size_5], ignore_index=True)\n",
    "result_size_ED['Data Size'] = pd.DataFrame({'ED':['200%','150%','100%','50%','20%']})\n",
    "result_size_ED.set_index('Data Size', inplace=True)\n",
    "result_size_ED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "790f190b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%  150%  100%  50%  20%\n",
       "size_1-8.6_61      0.0   0.0   0.0  0.0  0.0\n",
       "feeder_3-8.7_62    0.0   0.0   0.0  0.0  0.0\n",
       "gripper_1-8.8_63   0.0   0.0   0.0  0.0  0.0\n",
       "max_Vel_2-8.9_64   0.0   0.0   0.0  0.0  0.0\n",
       "Total              0.0   0.0   0.0  0.0  0.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_size_ED,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "bfd2b6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  200%  150%  100%  50%  20%\n",
       "size_1-8.6_61      0.0   0.0   0.0  0.0  0.0\n",
       "feeder_3-8.7_62    0.0   0.0   0.0  0.0  0.0\n",
       "gripper_1-8.8_63   0.0   0.0   0.0  0.0  0.0\n",
       "max_Vel_2-8.9_64   0.0   0.0   0.0  0.0  0.0\n",
       "Total              0.0   0.0   0.0  0.0  0.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_size_ED,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "03af12fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_5_ED.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_5_ED.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c74bc8a9",
   "metadata": {},
   "source": [
    "### RW - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "dce213e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RW_size_1 = run_RW_size(folder_path,files,startrow=839,size_p=2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_size_2 = run_RW_size(folder_path,files,startrow=839,size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_size_3 = run_RW_size(folder_path,files,startrow=839,size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_size_4 = run_RW_size(folder_path,files,startrow=839,size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RW_size_5 = run_RW_size(folder_path,files,startrow=839,size_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f876ded6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, con_2]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[cam_1_Y, cam_1_X, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_maxVel]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_3_X, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_maxVel, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_3_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, con_2]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_3_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_X, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[rob_2_maxVel, rob_2_supply, cam_1_Y]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>[rob_1_supply, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, rob_2_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, cam_1_Y]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, rob_1_supply, cam_1_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[cam_1_Y, cam_1_X, rob_2_maxVel]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_supply, rob_1_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "      <td>[cam_1_X, cam_1_Y, rob_2_supply]</td>\n",
       "      <td>[rob_2_supply, cam_1_X, rob_1_supply]</td>\n",
       "      <td>[rob_1_supply, rob_2_supply, cam_1_X]</td>\n",
       "      <td>[rob_2_maxVel, cam_1_X, rob_2_supply]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               size_1-1.2_1  \\\n",
       "Data Size                                     \n",
       "200%       [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "150%              [cam_1_Y, cam_1_X, con_2]   \n",
       "100%       [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "50%        [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "20%        [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "\n",
       "                                  feeder_3-1.3_2  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "150%       [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "100%       [rob_2_supply, rob_1_supply, cam_3_Y]   \n",
       "50%        [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%        [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "\n",
       "                                 gripper_1-1.4_3  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "150%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "50%        [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%        [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "\n",
       "                                 max_Vel_2-1.5_4  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "150%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "100%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "50%             [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "20%             [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                               size_1-1.6_5  \\\n",
       "Data Size                                     \n",
       "200%       [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "150%       [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "100%       [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "50%        [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "20%        [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "\n",
       "                                  feeder_3-1.7_6  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_2_supply, cam_3_X]   \n",
       "150%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "100%            [rob_1_supply, cam_3_X, cam_1_X]   \n",
       "50%        [rob_1_supply, cam_1_X, rob_2_supply]   \n",
       "20%        [cam_1_X, rob_1_supply, rob_2_supply]   \n",
       "\n",
       "                                      gripper_1-1.8_7  \\\n",
       "Data Size                                               \n",
       "200%            [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "150%                 [rob_1_supply, cam_1_X, cam_1_Y]   \n",
       "100%            [rob_1_supply, rob_2_maxVel, cam_1_X]   \n",
       "50%        [rob_1_supply, rob_2_supply, rob_2_maxVel]   \n",
       "20%             [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "\n",
       "                                 max_Vel_2-1.9_8  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "150%       [rob_2_maxVel, cam_1_Y, rob_2_supply]   \n",
       "100%            [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "50%             [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "20%             [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "\n",
       "                               size_1-2.2_9  \\\n",
       "Data Size                                     \n",
       "200%       [rob_2_supply, cam_1_X, cam_1_Y]   \n",
       "150%       [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "100%       [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "50%        [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "20%        [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "\n",
       "                                 feeder_3-2.3_10  ...  \\\n",
       "Data Size                                         ...   \n",
       "200%            [rob_1_supply, cam_1_Y, cam_1_X]  ...   \n",
       "150%       [rob_1_supply, cam_1_Y, rob_2_supply]  ...   \n",
       "100%       [rob_1_supply, cam_1_Y, rob_2_supply]  ...   \n",
       "50%             [rob_1_supply, cam_1_X, cam_1_Y]  ...   \n",
       "20%             [cam_1_X, rob_1_supply, cam_1_Y]  ...   \n",
       "\n",
       "                                gripper_1-7.8_55  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "150%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%        [rob_1_supply, cam_1_X, rob_2_supply]   \n",
       "20%        [rob_1_supply, cam_1_X, rob_2_supply]   \n",
       "\n",
       "                                max_Vel_2-7.9_56  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "150%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "100%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "50%             [rob_2_maxVel, cam_1_X, cam_1_Y]   \n",
       "20%        [rob_2_maxVel, cam_1_Y, rob_2_supply]   \n",
       "\n",
       "                              size_1-8.2_57  \\\n",
       "Data Size                                     \n",
       "200%       [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "150%       [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "100%       [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "50%        [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "20%        [cam_1_Y, cam_1_X, rob_2_maxVel]   \n",
       "\n",
       "                                 feeder_3-8.3_58  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, cam_1_Y, rob_2_supply]   \n",
       "150%       [rob_1_supply, cam_1_Y, rob_2_maxVel]   \n",
       "100%       [rob_1_supply, rob_2_supply, cam_3_Y]   \n",
       "50%        [rob_1_supply, cam_1_Y, rob_2_supply]   \n",
       "20%        [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "\n",
       "                                gripper_1-8.4_59  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%       [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "100%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%        [rob_1_supply, rob_2_supply, cam_1_Y]   \n",
       "20%        [rob_2_supply, rob_1_supply, cam_1_X]   \n",
       "\n",
       "                                max_Vel_2-8.5_60  \\\n",
       "Data Size                                          \n",
       "200%       [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "150%            [rob_2_maxVel, cam_1_Y, cam_1_X]   \n",
       "100%       [rob_2_maxVel, rob_2_supply, cam_1_X]   \n",
       "50%        [rob_2_maxVel, rob_2_supply, cam_1_Y]   \n",
       "20%        [rob_2_maxVel, cam_1_X, rob_2_supply]   \n",
       "\n",
       "                              size_1-8.6_61  \\\n",
       "Data Size                                     \n",
       "200%       [rob_2_supply, cam_1_Y, cam_1_X]   \n",
       "150%       [cam_1_Y, cam_1_X, rob_2_supply]   \n",
       "100%              [cam_1_X, cam_1_Y, con_2]   \n",
       "50%        [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "20%        [cam_1_X, cam_1_Y, rob_2_supply]   \n",
       "\n",
       "                                 feeder_3-8.7_62  \\\n",
       "Data Size                                          \n",
       "200%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "150%       [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "100%       [rob_2_supply, rob_1_supply, cam_3_X]   \n",
       "50%        [rob_2_supply, rob_1_supply, cam_1_Y]   \n",
       "20%        [rob_2_supply, cam_1_X, rob_1_supply]   \n",
       "\n",
       "                                gripper_1-8.8_63  \\\n",
       "Data Size                                          \n",
       "200%         [rob_1_supply, rob_2_supply, con_2]   \n",
       "150%       [rob_1_supply, cam_1_X, rob_2_supply]   \n",
       "100%       [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "50%        [rob_1_supply, cam_1_Y, rob_2_supply]   \n",
       "20%        [rob_1_supply, rob_2_supply, cam_1_X]   \n",
       "\n",
       "                                max_Vel_2-8.9_64  \n",
       "Data Size                                         \n",
       "200%       [rob_2_maxVel, rob_2_supply, cam_1_Y]  \n",
       "150%       [rob_2_maxVel, cam_1_X, rob_2_supply]  \n",
       "100%       [rob_2_maxVel, cam_1_X, rob_2_supply]  \n",
       "50%             [rob_2_maxVel, cam_1_X, cam_1_Y]  \n",
       "20%        [rob_2_maxVel, cam_1_X, rob_2_supply]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_size_RW = pd.DataFrame()\n",
    "result_size_RW = pd.concat([result_RW_size_1,result_RW_size_2,result_RW_size_3,result_RW_size_4,result_RW_size_5], ignore_index=True)\n",
    "result_size_RW['Data Size'] = pd.DataFrame({'RW':['200%','150%','100%','50%','20%']})\n",
    "result_size_RW.set_index('Data Size', inplace=True)\n",
    "result_size_RW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "bffa48db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.78125</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.96875</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     200%  150%     100%   50%   20%\n",
       "size_1-8.6_61     1.00000  1.00  1.00000  1.00  1.00\n",
       "feeder_3-8.7_62   0.00000  0.00  1.00000  0.00  0.00\n",
       "gripper_1-8.8_63  1.00000  1.00  1.00000  1.00  1.00\n",
       "max_Vel_2-8.9_64  1.00000  1.00  1.00000  1.00  1.00\n",
       "Total             0.78125  0.75  0.96875  0.75  0.75"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_size_RW,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b63da619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.53125</td>\n",
       "      <td>0.71875</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.671875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     200%     150%    100%   50%       20%\n",
       "size_1-8.6_61     0.00000  1.00000  1.0000  1.00  1.000000\n",
       "feeder_3-8.7_62   0.00000  0.00000  0.0000  0.00  0.000000\n",
       "gripper_1-8.8_63  1.00000  1.00000  1.0000  1.00  1.000000\n",
       "max_Vel_2-8.9_64  1.00000  1.00000  1.0000  1.00  1.000000\n",
       "Total             0.53125  0.71875  0.6875  0.75  0.671875"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_size_RW,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1f7bf7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_5_RW.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_5_RW.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943cf714",
   "metadata": {},
   "source": [
    "### RCD - Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a19870e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_RCD_size_1 = run_RCD_size(folder_path,files,startrow=839,size_p=2,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_size_2 = run_RCD_size(folder_path,files,startrow=839,size_p=1.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_size_3 = run_RCD_size(folder_path,files,startrow=839,size_p=1.0,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_size_4 = run_RCD_size(folder_path,files,startrow=839,size_p=0.5,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)\n",
    "result_RCD_size_5 = run_RCD_size(folder_path,files,startrow=839,size_p=0.20,nodes=nodes,edges_list=edges,key_nodes=check_nodes,colors=colors,pos=pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "5f429ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size_1-1.2_1</th>\n",
       "      <th>feeder_3-1.3_2</th>\n",
       "      <th>gripper_1-1.4_3</th>\n",
       "      <th>max_Vel_2-1.5_4</th>\n",
       "      <th>size_1-1.6_5</th>\n",
       "      <th>feeder_3-1.7_6</th>\n",
       "      <th>gripper_1-1.8_7</th>\n",
       "      <th>max_Vel_2-1.9_8</th>\n",
       "      <th>size_1-2.2_9</th>\n",
       "      <th>feeder_3-2.3_10</th>\n",
       "      <th>...</th>\n",
       "      <th>gripper_1-7.8_55</th>\n",
       "      <th>max_Vel_2-7.9_56</th>\n",
       "      <th>size_1-8.2_57</th>\n",
       "      <th>feeder_3-8.3_58</th>\n",
       "      <th>gripper_1-8.4_59</th>\n",
       "      <th>max_Vel_2-8.5_60</th>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200%</th>\n",
       "      <td>[EoL_3_Y, cam_1_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_X, cam_3_X]</td>\n",
       "      <td>[EoL_4_X, EoL_2_X]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[cam_1_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[rob_1_supply, EoL_2_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[cam_1_X, EoL_3_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_X, EoL_3_X]</td>\n",
       "      <td>[EoL_3_X, EoL_2_X]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_4_Y, cam_1_Y]</td>\n",
       "      <td>[cam_3_X, EoL_3_X, EoL_5_X]</td>\n",
       "      <td>[EoL_5_Y, EoL_5_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150%</th>\n",
       "      <td>[EoL_4_X, cam_1_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_2_X, EoL_5_Y]</td>\n",
       "      <td>[rob_1_supply, EoL_5_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_3_Y, cam_1_Y, cam_1_X]</td>\n",
       "      <td>[EoL_5_X, EoL_6_X]</td>\n",
       "      <td>[rob_1_supply, EoL_3_X]</td>\n",
       "      <td>[rob_2_maxVel, EoL_6_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_2_X, EoL_5_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_4_X, EoL_5_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_6_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_6_X]</td>\n",
       "      <td>[EoL_6_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_5_Y, EoL_3_Y]</td>\n",
       "      <td>[EoL_6_Y, rob_2_maxVel, EoL_6_X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100%</th>\n",
       "      <td>[cam_1_X, EoL_6_X, EoL_3_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_5_X]</td>\n",
       "      <td>[rob_1_supply, EoL_3_X, EoL_4_X]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[EoL_6_X, EoL_5_Y]</td>\n",
       "      <td>[cam_3_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_5_Y]</td>\n",
       "      <td>[EoL_2_X, cam_3_Y, EoL_6_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_4_X, EoL_2_X]</td>\n",
       "      <td>[rob_2_maxVel]</td>\n",
       "      <td>[cam_1_Y, EoL_5_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_5_X, EoL_4_Y]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X]</td>\n",
       "      <td>[cam_1_X, EoL_4_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_4_X, EoL_6_X]</td>\n",
       "      <td>[EoL_2_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_6_Y]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>[EoL_5_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_6_Y]</td>\n",
       "      <td>[EoL_5_X, EoL_4_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_6_X]</td>\n",
       "      <td>[EoL_4_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_2_X, EoL_6_Y, EoL_4_Y]</td>\n",
       "      <td>[EoL_4_X, EoL_5_Y, cam_1_X]</td>\n",
       "      <td>[EoL_6_X, EoL_1_Y, EoL_3_Y]</td>\n",
       "      <td>...</td>\n",
       "      <td>[EoL_6_X, EoL_5_X]</td>\n",
       "      <td>[EoL_6_X, EoL_2_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_2_X, cam_3_X, EoL_6_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_6_X]</td>\n",
       "      <td>[EoL_6_Y, EoL_6_X, cam_2_X]</td>\n",
       "      <td>[cam_1_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_3_X, EoL_5_X]</td>\n",
       "      <td>[EoL_2_Y, EoL_2_X]</td>\n",
       "      <td>[EoL_2_X, rob_2_maxVel]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>[EoL_1_X, EoL_1_Y]</td>\n",
       "      <td>[cam_2_Y, EoL_2_Y]</td>\n",
       "      <td>[EoL_5_X, rob_1_supply]</td>\n",
       "      <td>[EoL_2_Y, EoL_5_X]</td>\n",
       "      <td>[cam_3_X, cam_1_X]</td>\n",
       "      <td>[EoL_4_Y, EoL_2_X]</td>\n",
       "      <td>[cam_3_X, EoL_3_Y]</td>\n",
       "      <td>[EoL_4_Y, EoL_5_X, rob_2_maxVel]</td>\n",
       "      <td>[EoL_6_X, EoL_2_Y]</td>\n",
       "      <td>[EoL_3_X, cam_3_X]</td>\n",
       "      <td>...</td>\n",
       "      <td>[cam_3_X, EoL_1_Y]</td>\n",
       "      <td>[EoL_5_X, cam_3_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_3_X]</td>\n",
       "      <td>[cam_3_X, EoL_6_Y, EoL_3_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_4_X]</td>\n",
       "      <td>[EoL_3_Y, EoL_6_Y]</td>\n",
       "      <td>[cam_1_Y, EoL_4_Y]</td>\n",
       "      <td>[cam_3_X, EoL_3_X]</td>\n",
       "      <td>[EoL_5_X, EoL_5_Y]</td>\n",
       "      <td>[EoL_2_Y, cam_2_Y]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          size_1-1.2_1               feeder_3-1.3_2  \\\n",
       "Data Size                                                             \n",
       "200%                [EoL_3_Y, cam_1_Y]  [EoL_5_Y, EoL_6_X, cam_3_X]   \n",
       "150%                [EoL_4_X, cam_1_Y]  [EoL_6_Y, EoL_2_X, EoL_5_Y]   \n",
       "100%       [cam_1_X, EoL_6_X, EoL_3_X]           [EoL_4_Y, EoL_5_X]   \n",
       "50%                 [EoL_5_X, EoL_6_Y]           [EoL_4_X, EoL_6_Y]   \n",
       "20%                 [EoL_1_X, EoL_1_Y]           [cam_2_Y, EoL_2_Y]   \n",
       "\n",
       "                            gripper_1-1.4_3     max_Vel_2-1.5_4  \\\n",
       "Data Size                                                         \n",
       "200%                     [EoL_4_X, EoL_2_X]           [EoL_6_X]   \n",
       "150%                [rob_1_supply, EoL_5_Y]  [EoL_6_Y, EoL_6_X]   \n",
       "100%       [rob_1_supply, EoL_3_X, EoL_4_X]      [rob_2_maxVel]   \n",
       "50%                      [EoL_5_X, EoL_4_X]  [EoL_2_Y, EoL_6_X]   \n",
       "20%                 [EoL_5_X, rob_1_supply]  [EoL_2_Y, EoL_5_X]   \n",
       "\n",
       "                          size_1-1.6_5      feeder_3-1.7_6  \\\n",
       "Data Size                                                    \n",
       "200%                [cam_1_Y, EoL_3_Y]  [EoL_6_Y, EoL_6_X]   \n",
       "150%       [EoL_3_Y, cam_1_Y, cam_1_X]  [EoL_5_X, EoL_6_X]   \n",
       "100%                [EoL_6_X, EoL_5_Y]  [cam_3_Y, EoL_5_Y]   \n",
       "50%                 [EoL_3_Y, EoL_2_Y]  [EoL_4_X, EoL_6_X]   \n",
       "20%                 [cam_3_X, cam_1_X]  [EoL_4_Y, EoL_2_X]   \n",
       "\n",
       "                       gripper_1-1.8_7                   max_Vel_2-1.9_8  \\\n",
       "Data Size                                                                  \n",
       "200%       [EoL_2_Y, EoL_3_Y, EoL_6_Y]                         [EoL_6_Y]   \n",
       "150%           [rob_1_supply, EoL_3_X]           [rob_2_maxVel, EoL_6_X]   \n",
       "100%                [EoL_5_X, EoL_6_X]                         [EoL_4_Y]   \n",
       "50%                 [EoL_4_X, EoL_3_Y]       [EoL_2_X, EoL_6_Y, EoL_4_Y]   \n",
       "20%                 [cam_3_X, EoL_3_Y]  [EoL_4_Y, EoL_5_X, rob_2_maxVel]   \n",
       "\n",
       "                          size_1-2.2_9              feeder_3-2.3_10  ...  \\\n",
       "Data Size                                                            ...   \n",
       "200%       [EoL_6_Y, EoL_3_Y, EoL_4_X]  [EoL_6_Y, EoL_3_Y, EoL_4_X]  ...   \n",
       "150%                [EoL_3_Y, EoL_6_X]           [EoL_2_X, EoL_5_X]  ...   \n",
       "100%                [EoL_6_Y, EoL_5_Y]  [EoL_2_X, cam_3_Y, EoL_6_Y]  ...   \n",
       "50%        [EoL_4_X, EoL_5_Y, cam_1_X]  [EoL_6_X, EoL_1_Y, EoL_3_Y]  ...   \n",
       "20%                 [EoL_6_X, EoL_2_Y]           [EoL_3_X, cam_3_X]  ...   \n",
       "\n",
       "                           gripper_1-7.8_55    max_Vel_2-7.9_56  \\\n",
       "Data Size                                                         \n",
       "200%       [rob_1_supply, EoL_2_Y, EoL_3_Y]  [EoL_6_X, EoL_6_Y]   \n",
       "150%            [EoL_4_X, EoL_5_Y, EoL_5_X]           [EoL_6_Y]   \n",
       "100%                     [EoL_4_X, EoL_2_X]      [rob_2_maxVel]   \n",
       "50%                      [EoL_6_X, EoL_5_X]  [EoL_6_X, EoL_2_X]   \n",
       "20%                      [cam_3_X, EoL_1_Y]  [EoL_5_X, cam_3_X]   \n",
       "\n",
       "                         size_1-8.2_57              feeder_3-8.3_58  \\\n",
       "Data Size                                                             \n",
       "200%       [cam_1_X, EoL_3_Y, EoL_5_X]  [EoL_2_Y, EoL_5_X, EoL_3_X]   \n",
       "150%       [EoL_5_Y, EoL_6_X, EoL_4_Y]           [EoL_4_X, EoL_6_X]   \n",
       "100%                [cam_1_Y, EoL_5_X]  [EoL_2_Y, EoL_5_Y, EoL_6_X]   \n",
       "50%                 [EoL_6_Y, EoL_3_X]  [EoL_2_X, cam_3_X, EoL_6_X]   \n",
       "20%                 [EoL_3_Y, EoL_3_X]  [cam_3_X, EoL_6_Y, EoL_3_X]   \n",
       "\n",
       "             gripper_1-8.4_59             max_Vel_2-8.5_60  \\\n",
       "Data Size                                                    \n",
       "200%       [EoL_3_X, EoL_2_X]                    [EoL_6_X]   \n",
       "150%       [EoL_6_X, EoL_6_Y]                    [EoL_6_X]   \n",
       "100%       [EoL_5_X, EoL_4_Y]           [EoL_6_Y, EoL_6_X]   \n",
       "50%        [EoL_4_Y, EoL_6_X]  [EoL_6_Y, EoL_6_X, cam_2_X]   \n",
       "20%        [EoL_3_Y, EoL_4_X]           [EoL_3_Y, EoL_6_Y]   \n",
       "\n",
       "                         size_1-8.6_61              feeder_3-8.7_62  \\\n",
       "Data Size                                                             \n",
       "200%       [EoL_6_Y, EoL_4_Y, cam_1_Y]  [cam_3_X, EoL_3_X, EoL_5_X]   \n",
       "150%                [EoL_6_Y, EoL_5_Y]           [EoL_5_X, EoL_2_Y]   \n",
       "100%       [cam_1_X, EoL_4_Y, EoL_4_X]  [EoL_6_Y, EoL_4_X, EoL_6_X]   \n",
       "50%                 [cam_1_X, EoL_5_Y]           [EoL_3_X, EoL_5_X]   \n",
       "20%                 [cam_1_Y, EoL_4_Y]           [cam_3_X, EoL_3_X]   \n",
       "\n",
       "             gripper_1-8.8_63                  max_Vel_2-8.9_64  \n",
       "Data Size                                                        \n",
       "200%       [EoL_5_Y, EoL_5_X]           [rob_2_maxVel, EoL_6_Y]  \n",
       "150%       [EoL_5_Y, EoL_3_Y]  [EoL_6_Y, rob_2_maxVel, EoL_6_X]  \n",
       "100%       [EoL_2_X, EoL_6_Y]                         [EoL_6_Y]  \n",
       "50%        [EoL_2_Y, EoL_2_X]           [EoL_2_X, rob_2_maxVel]  \n",
       "20%        [EoL_5_X, EoL_5_Y]                [EoL_2_Y, cam_2_Y]  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_size_RCD = pd.DataFrame()\n",
    "result_size_RCD = pd.concat([result_RCD_size_1,result_RCD_size_2,result_RCD_size_3,result_RCD_size_4,result_RCD_size_5], ignore_index=True)\n",
    "result_size_RCD['Data Size'] = pd.DataFrame({'RCD':['200%','150%','100%','50%','20%']})\n",
    "result_size_RCD.set_index('Data Size', inplace=True)\n",
    "result_size_RCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "85500f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.375</td>\n",
       "      <td>0.390625</td>\n",
       "      <td>0.40625</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.234375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   200%      150%     100%     50%       20%\n",
       "size_1-8.6_61     1.000  0.000000  1.00000  1.0000  1.000000\n",
       "feeder_3-8.7_62   1.000  0.000000  0.00000  0.0000  1.000000\n",
       "gripper_1-8.8_63  0.000  0.000000  0.00000  0.0000  0.000000\n",
       "max_Vel_2-8.9_64  1.000  1.000000  0.00000  1.0000  0.000000\n",
       "Total             0.375  0.390625  0.40625  0.3125  0.234375"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_3top = results_top_3(result_size_RCD,abnormal_sets)\n",
    "result_data_3top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "45198971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>200%</th>\n",
       "      <th>150%</th>\n",
       "      <th>100%</th>\n",
       "      <th>50%</th>\n",
       "      <th>20%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size_1-8.6_61</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feeder_3-8.7_62</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gripper_1-8.8_63</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_Vel_2-8.9_64</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.28125</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>0.078125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    200%     150%      100%     50%       20%\n",
       "size_1-8.6_61     0.0000  0.00000  1.000000  1.0000  1.000000\n",
       "feeder_3-8.7_62   1.0000  0.00000  0.000000  0.0000  1.000000\n",
       "gripper_1-8.8_63  0.0000  0.00000  0.000000  0.0000  0.000000\n",
       "max_Vel_2-8.9_64  1.0000  0.00000  0.000000  0.0000  0.000000\n",
       "Total             0.1875  0.28125  0.234375  0.1875  0.078125"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data_1top = results_top_1(result_size_RCD,abnormal_sets)\n",
    "result_data_1top.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "59607a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data_3top.to_csv(os.path.join(main_dir, 'result_data_3top_5_RCD.csv'))\n",
    "result_data_1top.to_csv(os.path.join(main_dir, 'result_data_1top_5_RCD.csv'))"
   ]
  }
 ],
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