Part 4-2. Cluster 기반 Mapping_교향곡.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3a66891",
   "metadata": {},
   "source": [
    "# PART 4-2 < Cluster 기반 Mapping : 교향곡 >"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5661bc4",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "833a10fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "090878f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_solo = pd.read_csv('data/concert_교향곡_code3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b963f6",
   "metadata": {},
   "source": [
    "# 분포 시각화 및 Mapping 을 위한 테이블 분할 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9047d15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# little preprocessing \n",
    "\n",
    "data_solo = data_solo.iloc[:,1:]\n",
    "data_solo['열'] = data_solo['열'].str.replace('열', '')\n",
    "data_solo = data_solo[~data_solo['열'].isin(['box'])]\n",
    "data_solo['열'] = data_solo['열'].fillna(0)\n",
    "data_solo['열'] = data_solo['열'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c97962d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 박스 별 구분 \n",
    "\n",
    "solo_C = data_solo[data_solo['블록(박스)'] == 'C']\n",
    "solo_B = data_solo[data_solo['블록(박스)'] == 'B']\n",
    "solo_D = data_solo[data_solo['블록(박스)'] == 'D']\n",
    "solo_A = data_solo[data_solo['블록(박스)'] == 'A']\n",
    "solo_E = data_solo[data_solo['블록(박스)'] == 'E']\n",
    "solo_G = data_solo[data_solo['블록(박스)'] == 'G']\n",
    "solo_M = data_solo[data_solo['블록(박스)'] == 'M']\n",
    "solo_F = data_solo[data_solo['블록(박스)'] == 'F']\n",
    "solo_N = data_solo[data_solo['블록(박스)'] == 'N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0bb4057e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 해당 박스의 층별 구분 \n",
    "solo_C_f1 = solo_C[solo_C['층'] == 1]\n",
    "solo_C_f2 = solo_C[solo_C['층'] == 2]\n",
    "solo_C_f3 = solo_C[solo_C['층'] == 3]\n",
    "\n",
    "solo_B_f1 = solo_B[solo_B['층'] == 1]\n",
    "solo_B_f2 = solo_B[solo_B['층'] == 2]\n",
    "solo_B_f3 = solo_B[solo_B['층'] == 3]\n",
    "\n",
    "solo_D_f1 = solo_D[solo_D['층'] == 1]\n",
    "solo_D_f2 = solo_D[solo_D['층'] == 2]\n",
    "solo_D_f3 = solo_D[solo_D['층'] == 3]\n",
    "\n",
    "solo_A_f1 = solo_A[solo_A['층'] == 1]\n",
    "solo_A_f2 = solo_A[solo_A['층'] == 2]\n",
    "solo_A_f3 = solo_A[solo_A['층'] == 3]\n",
    "\n",
    "solo_E_f1 = solo_E[solo_E['층'] == 1]\n",
    "solo_E_f2 = solo_E[solo_E['층'] == 2]\n",
    "solo_E_f3 = solo_E[solo_E['층'] == 3]\n",
    "\n",
    "solo_G_f1 = solo_G[solo_G['층'] == 1]\n",
    "solo_G_f2 = solo_G[solo_G['층'] == 2]\n",
    "solo_G_f3 = solo_G[solo_G['층'] == 3]\n",
    "\n",
    "solo_M_f1 = solo_M[solo_M['층'] == 1]\n",
    "solo_M_f2 = solo_M[solo_M['층'] == 2]\n",
    "solo_M_f3 = solo_M[solo_M['층'] == 3]\n",
    "\n",
    "solo_F_f1 = solo_F[solo_F['층'] == 1]\n",
    "solo_F_f2 = solo_F[solo_F['층'] == 2]\n",
    "solo_F_f3 = solo_F[solo_F['층'] == 3]\n",
    "\n",
    "solo_N_f1 = solo_N[solo_N['층'] == 1]\n",
    "solo_N_f2 = solo_N[solo_N['층'] == 2]\n",
    "solo_N_f3 = solo_N[solo_N['층'] == 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631c3d57",
   "metadata": {},
   "source": [
    "# 분포 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a4bb35d",
   "metadata": {},
   "source": [
    "**나누어진 테이블 기반 시각화**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a31ee01",
   "metadata": {},
   "source": [
    "- 클러스터별 '열' 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d986594",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing \n",
    "datas = [solo_C_f1,solo_C_f2,solo_C_f3,solo_B_f3,solo_B_f2,solo_B_f1,solo_A_f3,solo_A_f2,solo_A_f1,solo_D_f1,solo_D_f2,solo_D_f3,\n",
    "         solo_E_f1,solo_E_f2,solo_E_f3,solo_F_f1,solo_F_f2,solo_F_f3,solo_G_f1,solo_G_f2,solo_G_f3,solo_M_f1,solo_M_f2,solo_M_f3,\n",
    "         solo_N_f1,solo_N_f2,solo_N_f3]\n",
    "datas_title = ['solo_C_f1','solo_C_f2','solo_C_f3','solo_B_f3','solo_B_f2','solo_B_f1','solo_A_f3','solo_A_f2','solo_A_f1','solo_D_f1','solo_D_f2','solo_D_f3',\n",
    "         'solo_E_f1','solo_E_f2','solo_E_f3','solo_F_f1','solo_F_f2','solo_F_f3','solo_G_f1','solo_G_f2','solo_G_f3','solo_M_f1','solo_M_f2','solo_M_f3',\n",
    "         'solo_N_f1','solo_N_f2','solo_N_f3']\n",
    "\n",
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.boxplot(y='열',x='cluster',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)])\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390be70d",
   "metadata": {},
   "source": [
    "- 클러스터별 '행'(자리번호) 선호도 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "892e90d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x4200 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col_n = 5\n",
    "row_n = 6\n",
    "\n",
    "fig, ax = plt.subplots(ncols=col_n, nrows=row_n, figsize=(20,row_n*7))\n",
    "n = 0\n",
    "for i in  range(len(datas)):\n",
    "    if len(datas[i]) != 0:\n",
    "        p = sns.countplot(x='자리번호',data=datas[i], ax=ax[int(n/col_n),int(n%col_n)],palette='pastel')\n",
    "        p.set_title(datas_title[i])\n",
    "    n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8796a1",
   "metadata": {},
   "source": [
    "# Mapping \n",
    "- 위 분포 시각화를 통해 콘서트홀 좌석배치표에 맵핑\n",
    "- 해당 맵핑 결과를 기반으로 데이터 라벨링"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "234713bf",
   "metadata": {},
   "source": [
    "### 1층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a82c4e",
   "metadata": {},
   "source": [
    "- B 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4157cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f1['labels'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "867d5919",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'S' if (row['열'] <= 5 )& (row['열'] >= 1) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'R' if (row['열'] > 5) & (row['열'] <= 12) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'S' if (row['열'] > 12) & (row['열'] <= 17) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'X' if (row['열'] > 17) & (row['열'] <= 20) else row['labels'], axis=1)\n",
    "solo_B_f1['labels'] = solo_B_f1.apply(lambda row: 'A' if (row['열'] > 20) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14b41ba5",
   "metadata": {},
   "source": [
    "- C 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e0fe4f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f1['labels'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e187edf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'S' if (row['열'] <= 5 )& (row['열'] >= 1) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'R' if (row['열'] > 5) & (row['열'] <= 12) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'S' if (row['열'] > 12) & (row['열'] <= 17) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'X' if (row['열'] > 17) & (row['열'] <= 20) else row['labels'], axis=1)\n",
    "solo_C_f1['labels'] = solo_C_f1.apply(lambda row: 'A' if (row['열'] > 20) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d769f9",
   "metadata": {},
   "source": [
    "- D 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52c2252a",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f1['labels'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c3e95c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'S' if (row['열'] <= 5 )& (row['열'] >= 1) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'R' if (row['열'] > 5) & (row['열'] <= 12) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'S' if (row['열'] > 12) & (row['열'] <= 17) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'X' if (row['열'] > 17) & (row['열'] <= 20) else row['labels'], axis=1)\n",
    "solo_D_f1['labels'] = solo_D_f1.apply(lambda row: 'A' if (row['열'] > 20) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7a2eac",
   "metadata": {},
   "source": [
    "- A 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f77f775",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f1['labels'] = 'B'\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'A' if (row['자리번호'] >= 4) else row['labels'], axis=1)\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'X' if (row['자리번호'] >= 6) else row['labels'], axis=1)\n",
    "solo_A_f1['labels'] = solo_A_f1.apply(lambda row: 'S' if (row['자리번호'] >= 9) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca5e439",
   "metadata": {},
   "source": [
    "- E 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa758452",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f1['labels'] = 'B'\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'S' if (row['자리번호'] >= 4) else row['labels'], axis=1)\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'X' if (row['자리번호'] >= 6) else row['labels'], axis=1)\n",
    "solo_E_f1['labels'] = solo_E_f1.apply(lambda row: 'A' if (row['자리번호'] >= 9) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a19c12",
   "metadata": {},
   "source": [
    "### 2층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd26268",
   "metadata": {},
   "source": [
    "- B,c,d 블럭 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c742b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f2['labels'] = 'A'\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'X' if (row['열'] <= 6) else row['labels'], axis=1)\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'S' if (row['열'] == 3) else row['labels'], axis=1)\n",
    "solo_B_f2['labels'] = solo_B_f2.apply(lambda row: 'R' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7db23879",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f2['labels'] = 'A'\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'X' if (row['열'] <= 6) else row['labels'], axis=1)\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'S' if (row['열'] == 3) else row['labels'], axis=1)\n",
    "solo_C_f2['labels'] = solo_C_f2.apply(lambda row: 'R' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9cbe9dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f2['labels'] = 'A'\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'X' if (row['열'] <= 6) else row['labels'], axis=1)\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'S' if (row['열'] == 3) else row['labels'], axis=1)\n",
    "solo_D_f2['labels'] = solo_D_f2.apply(lambda row: 'R' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eff4bdb",
   "metadata": {},
   "source": [
    "- A 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "71c91c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f2['labels'] = 'B'\n",
    "solo_A_f2['labels'] = solo_A_f2.apply(lambda row: 'A' if (row['열'] <= 6) else row['labels'], axis=1)\n",
    "solo_A_f2['labels'] = solo_A_f2.apply(lambda row: 'X' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ddf84f",
   "metadata": {},
   "source": [
    "- E 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "403defb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f2['labels'] = 'B'\n",
    "solo_E_f2['labels'] = solo_E_f2.apply(lambda row: 'A' if (row['열'] <= 6) else row['labels'], axis=1)\n",
    "solo_E_f2['labels'] = solo_E_f2.apply(lambda row: 'X' if (row['열'] < 3) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c899422",
   "metadata": {},
   "source": [
    "### 3층 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ba09cf",
   "metadata": {},
   "source": [
    "- b,c,d,e,f 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91e95aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_B_f3['labels'] = 'C'\n",
    "solo_B_f3['labels'] = solo_B_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b878398",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_C_f3['labels'] = 'C'\n",
    "solo_C_f3['labels'] = solo_C_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d835c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_D_f3['labels'] = 'C'\n",
    "solo_D_f3['labels'] = solo_D_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e76b7c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_E_f3['labels'] = 'C'\n",
    "solo_E_f3['labels'] = solo_E_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ace7359",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_F_f3['labels'] = 'C'\n",
    "solo_F_f3['labels'] = solo_F_f3.apply(lambda row: 'B' if (row['열'] < 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e50373",
   "metadata": {},
   "source": [
    "- a, g 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cacc219c",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_A_f3['labels'] = 'Y'\n",
    "solo_A_f3['labels'] = solo_A_f3.apply(lambda row: 'C' if (row['열'] <= 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd58d3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_G_f3['labels'] = 'Y'\n",
    "solo_G_f3['labels'] = solo_G_f3.apply(lambda row: 'C' if (row['열'] <= 5) else row['labels'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e47c8f",
   "metadata": {},
   "source": [
    "- m,n 블럭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "97a470f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "solo_M_f3['labels'] = 'Y'\n",
    "solo_N_f3['labels'] = 'Y'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75672a2",
   "metadata": {},
   "source": [
    "# 데이터 concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2174d517",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff = pd.concat([solo_A_f1,solo_A_f2])\n",
    "ddff = pd.concat([ddff,solo_A_f3])\n",
    "ddff = pd.concat([ddff,solo_B_f1])\n",
    "ddff = pd.concat([ddff,solo_B_f2])\n",
    "ddff = pd.concat([ddff,solo_B_f3])\n",
    "ddff = pd.concat([ddff,solo_C_f1])\n",
    "ddff = pd.concat([ddff,solo_C_f2])\n",
    "ddff = pd.concat([ddff,solo_C_f3])\n",
    "ddff = pd.concat([ddff,solo_D_f1])\n",
    "ddff = pd.concat([ddff,solo_D_f2])\n",
    "ddff = pd.concat([ddff,solo_D_f3])\n",
    "ddff = pd.concat([ddff,solo_E_f1])\n",
    "ddff = pd.concat([ddff,solo_E_f2])\n",
    "ddff = pd.concat([ddff,solo_E_f3])\n",
    "ddff = pd.concat([ddff,solo_F_f3])\n",
    "ddff = pd.concat([ddff,solo_G_f3])\n",
    "ddff = pd.concat([ddff,solo_M_f3])\n",
    "ddff = pd.concat([ddff,solo_N_f3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbfab04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddff.to_csv('data/교향곡_라벨_code4.csv')"
   ]
  }
 ],
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  "kernelspec": {
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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