post-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4792506c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from shapely import wkt\n",
    "# from pygmo import hypervolume\n",
    "# import pygmo as pg\n",
    "# from pathlib2 import Path\n",
    "import geopandas as gpd\n",
    "# from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1d7786f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre read\n"
     ]
    }
   ],
   "source": [
    "def pre_read():\n",
    "#     如果报错记得检查文件路径\n",
    "    print('pre read')\n",
    "    with open(\"./DATA/real_life_case_network/prepared/upstream_set.pkl\", \"rb\") as tf:\n",
    "        upstream_set = pickle.load(tf)\n",
    "    tf.close()\n",
    "    with open(\"./DATA/real_life_case_network/prepared/upstream_arr.pkl\", \"rb\") as tf:\n",
    "        upstream_arr = pickle.load(tf)\n",
    "    tf.close()\n",
    "    with open(\"./DATA/real_life_case_network/prepared/upstream_links_set.pkl\", \"rb\") as tf:\n",
    "        upstream_links_set = pickle.load(tf)\n",
    "    tf.close()\n",
    "\n",
    "    fn = './DATA/real_life_case_network/prepared/pipe_TM_clean_relabel.pkl'\n",
    "    relabeled_G = nx.read_gpickle(fn)\n",
    "\n",
    "    mapping_file = open('./DATA/real_life_case_network/prepared/label_mapping.pkl',\n",
    "                        'rb')  # 以二进制读模式（rb）打开pkl文件\n",
    "    data = pickle.load(mapping_file)  # 读取存储的pickle文件\n",
    "    pos_data = dict(zip(data.values(), data.keys()))\n",
    "\n",
    "    return upstream_arr, upstream_set,upstream_links_set, relabeled_G, pos_data\n",
    "upstream_arr, upstream_set,upstream_links_set, relabeled_G, pos_data = pre_read()\n",
    "\n",
    "def solution_to_objective_values(solution):\n",
    "    # extract objective values from solution\n",
    "    objective_values = []\n",
    "    objective_values.extend([individual[1:3] for individual in solution])\n",
    "    objective_values = np.array(objective_values)\n",
    "    return objective_values\n",
    "\n",
    "def cal_norm_para(min_max_df):\n",
    "    coverage_min=min_max_df['coverage'].min()\n",
    "    coverage_max=min_max_df['coverage'].max()\n",
    "    resolution_min=min_max_df['resolution'].min()\n",
    "    resolution_max=min_max_df['resolution'].max()\n",
    "    norm_params={'coverage':[coverage_min,coverage_max],\n",
    "            'resolution':[resolution_min,resolution_max]}\n",
    "    return norm_params\n",
    "\n",
    "def normalize(values, norm_params, col):\n",
    "    mini, maxi = norm_params[col]\n",
    "    return (values - mini)/(maxi - mini)\n",
    "\n",
    "ref_point=[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77af8e7a",
   "metadata": {},
   "outputs": [
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       "  [9, -65, 3.0545436529843744, [15, 25, 28, 36, 41, 42, 63, 66, 76]],\n",
       "  [9, -66, 3.1204994466397284, [28, 34, 39, 46, 62, 63, 83, 88, 94]],\n",
       "  [7, -8, 0.25, [40, 52, 65, 75, 81, 90, 92]]]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn='.//TESTOUTPUT//synthetic_case//greedy_new//100//0//max_sensor_10_Lmax_20_new_plans_5//4//population.pkl'\n",
    "with open(fn, \"rb\") as tf:\n",
    "    solution = pickle.load(tf)\n",
    "solution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b9cfa0",
   "metadata": {},
   "source": [
    "# synthetic case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "77e196e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.25\n",
      "33.26\n",
      "60.26\n",
      "91.56\n",
      "96.81\n",
      "150.66\n",
      "186.13\n",
      "223.31\n",
      "253.49\n",
      "330.49\n",
      "685.55\n",
      "1190.3\n",
      "1951.67\n",
      "3052.6\n"
     ]
    }
   ],
   "source": [
    "# read time.txt file,修改new_plans\n",
    "sizes=[100,200,300,400,500,600,700,800,900,1000,1500,2000,2500,3000]\n",
    "for size in sizes:\n",
    "    max_sensor=int(size/10)\n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_new//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20_new_plans_30//0//time.txt'\n",
    "    f=open(path_new,'r')\n",
    "    print(round(float(f.read()),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81979f26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.69\n",
      "733.61\n",
      "2532.55\n",
      "6176.07\n",
      "13503.35\n",
      "19734.28\n",
      "31385.43\n",
      "45900.06\n",
      "64501.67\n",
      "113634.28\n"
     ]
    }
   ],
   "source": [
    "# read time.txt file,修改new_plans\n",
    "sizes=[100,200,300,400,500,600,700,800,900,1000] #,1500,2000,2500,3000]\n",
    "for size in sizes:\n",
    "    max_sensor=int(size/10)\n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_original//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20//0//time.txt'\n",
    "    f=open(path_new,'r')\n",
    "    print(round(float(f.read()),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed9b8c5",
   "metadata": {},
   "source": [
    "#### when OG cannot generate solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92755364",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-1fa07bb72707>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'coverage': [-3000.0, -300.0], 'resolution': [0.0, 3.5998760533901106]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new greedy result to get the min_max_df, network size from 1500-3000\n",
    "min_max_df=pd.DataFrame(columns=['coverage','resolution'])\n",
    "new_plans_all=[5,10,15,20,25,30]\n",
    "size=3000\n",
    "max_sensor=int(size/10)\n",
    "for new_plans in new_plans_all:\n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_new//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path_new, 'rb') as f:\n",
    "        result_new = pickle.load(f)\n",
    "\n",
    "    obj_new=solution_to_objective_values(result_new)\n",
    "    obj_new_df=pd.DataFrame(obj_new, columns=['coverage', 'resolution'])\n",
    "    min_max_df=min_max_df.append(obj_new_df)\n",
    "\n",
    "norm_params=cal_norm_para(min_max_df)\n",
    "norm_params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5517622e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_plans: 5 hv: 0.1503408570226929\n",
      "HV for new greedy with 5 new plans generated and network size of 3000 is 0.15.\n",
      "new_plans: 10 hv: 0.20922770896493614\n",
      "HV for new greedy with 10 new plans generated and network size of 3000 is 0.209.\n",
      "new_plans: 15 hv: 0.24137334465349108\n",
      "HV for new greedy with 15 new plans generated and network size of 3000 is 0.241.\n",
      "new_plans: 20 hv: 0.25215169132322396\n",
      "HV for new greedy with 20 new plans generated and network size of 3000 is 0.252.\n",
      "new_plans: 25 hv: 0.2619627745149813\n",
      "HV for new greedy with 25 new plans generated and network size of 3000 is 0.262.\n",
      "new_plans: 30 hv: 0.2496893944714817\n",
      "HV for new greedy with 30 new plans generated and network size of 3000 is 0.25.\n"
     ]
    }
   ],
   "source": [
    "for new_plans in new_plans_all:\n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_new//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path_new, 'rb') as f:\n",
    "        result_new = pickle.load(f)\n",
    "    obj_new=solution_to_objective_values(result_new)\n",
    "    obj_norm=obj_new.copy()  # obj should be array\n",
    "    for i in range(2):\n",
    "        obj_norm[:,i]=normalize(obj_new[:,i], norm_params, ['coverage', 'resolution'][i])\n",
    "    hv=pg.hypervolume(obj_norm).compute(ref_point)\n",
    "    print('new_plans:', new_plans, 'hv:', hv)\n",
    "    print('HV for new greedy with {} new plans generated and network size of {} is {}.'.format(new_plans, size,round(hv,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46dabcb",
   "metadata": {},
   "source": [
    "#### when OG can generaete solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30709f7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_new_df)\n",
      "<ipython-input-15-2af54c0ed6ac>:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_original_df)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'coverage': [-900.0, -90.0], 'resolution': [0.0, 3.6251934465714655]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# network size : 100, 500, 1000\n",
    "min_max_df=pd.DataFrame(columns=['coverage','resolution'])\n",
    "new_plans_all=[5,10,15,20,25,30]\n",
    "size=900\n",
    "max_sensor=int(size/10)\n",
    "for new_plans in new_plans_all:   \n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_new//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path_new, 'rb') as f:\n",
    "        result_new = pickle.load(f)\n",
    "\n",
    "    obj_new=solution_to_objective_values(result_new)\n",
    "    obj_new_df=pd.DataFrame(obj_new, columns=['coverage', 'resolution'])\n",
    "    min_max_df=min_max_df.append(obj_new_df)\n",
    "\n",
    "path_original='.//TESTOUTPUT//synthetic_case//greedy_original//'+str(size)+'//0//'+'max_sensor_' + str(max_sensor) + '_Lmax_20'+'//0//solution.pkl'\n",
    "with open(path_original, 'rb') as f:\n",
    "    greedy_original = pickle.load(f)\n",
    "\n",
    "n=len(greedy_original)\n",
    "result_original=greedy_original[n-1]\n",
    "\n",
    "obj_original=solution_to_objective_values(result_original)\n",
    "obj_original_df=pd.DataFrame(obj_original, columns=['coverage', 'resolution'])\n",
    "min_max_df=min_max_df.append(obj_original_df)\n",
    "\n",
    "norm_params=cal_norm_para(min_max_df)\n",
    "norm_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb3bbe94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_plans: 5 hv: 0.18459288873326915\n",
      "HV for new greedy with 5 new plans generated and network size of 900 is 0.185.\n",
      "new_plans: 10 hv: 0.2202921867953848\n",
      "HV for new greedy with 10 new plans generated and network size of 900 is 0.22.\n",
      "new_plans: 15 hv: 0.2631636600807501\n",
      "HV for new greedy with 15 new plans generated and network size of 900 is 0.263.\n",
      "new_plans: 20 hv: 0.26289442174995103\n",
      "HV for new greedy with 20 new plans generated and network size of 900 is 0.263.\n",
      "new_plans: 25 hv: 0.25277864184082355\n",
      "HV for new greedy with 25 new plans generated and network size of 900 is 0.253.\n",
      "new_plans: 30 hv: 0.2765613710625661\n",
      "HV for new greedy with 30 new plans generated and network size of 900 is 0.277.\n",
      "HV for original greedy with network size of 900 is 0.301.\n"
     ]
    }
   ],
   "source": [
    "for new_plans in new_plans_all:\n",
    "    max_sensor=int(size/10)\n",
    "    path_new='.//TESTOUTPUT//synthetic_case//greedy_new//' + str(size) + '//0//' + 'max_sensor_' + str(max_sensor) + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path_new, 'rb') as f:\n",
    "        result_new = pickle.load(f)\n",
    "    obj_new=solution_to_objective_values(result_new)\n",
    "    obj_norm=obj_new.copy()  # obj should be array\n",
    "    for i in range(2):\n",
    "        obj_norm[:,i]=normalize(obj_new[:,i], norm_params, ['coverage', 'resolution'][i])\n",
    "    hv=pg.hypervolume(obj_norm).compute(ref_point)\n",
    "    print('new_plans:', new_plans, 'hv:', hv)\n",
    "    print('HV for new greedy with {} new plans generated and network size of {} is {}.'.format(new_plans, size,round(hv,3)))\n",
    "\n",
    "obj_norm=obj_original.copy()  # obj should be array\n",
    "for i in range(2):\n",
    "    obj_norm[:,i]=normalize(obj_original[:,i], norm_params, ['coverage', 'resolution'][i])\n",
    "hv=pg.hypervolume(obj_norm).compute(ref_point)\n",
    "print('HV for original greedy with network size of {} is {}.'.format(size,round(hv,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065e2cd6",
   "metadata": {},
   "source": [
    "# real-world case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "561690bb",
   "metadata": {},
   "source": [
    "#### time and HV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b3b1f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.07\n",
      "72.55\n",
      "121.49\n",
      "178.7\n",
      "235.17\n",
      "327.76\n"
     ]
    }
   ],
   "source": [
    "# 时间\n",
    "new_plans_all=[5,10,15,20,25,30]\n",
    "for new_plans in new_plans_all:\n",
    "    path='.//TESTOUTPUT//real_case//greedy_new//max_sensor_100' + '_Lmax_20_new_plans_'+str(new_plans)+'//0//time.txt'\n",
    "    f=open(path,'r')\n",
    "    print(round(float(f.read()),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24b4e1dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n",
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n",
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n",
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n",
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n",
      "<ipython-input-19-7325410f3266>:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  min_max_df=min_max_df.append(obj_df)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'coverage': [-4204.0, -100.0], 'resolution': [0.0, 6.537459549227607]}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HV\n",
    "min_max_df=pd.DataFrame(columns=['coverage','resolution'])\n",
    "new_plans_all=[5,10,15,20,25,30]\n",
    "\n",
    "for new_plans in new_plans_all:\n",
    "    path='.//TESTOUTPUT//real_case//greedy_new//max_sensor_100' + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path, 'rb') as f:\n",
    "        result = pickle.load(f)\n",
    "\n",
    "    obj=solution_to_objective_values(result)\n",
    "    obj_df=pd.DataFrame(obj, columns=['coverage', 'resolution'])\n",
    "    min_max_df=min_max_df.append(obj_df)\n",
    "\n",
    "norm_params=cal_norm_para(min_max_df)\n",
    "norm_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99b825fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new_plans: 5 hv: 0.222\n",
      "new_plans: 10 hv: 0.24\n",
      "new_plans: 15 hv: 0.258\n",
      "new_plans: 20 hv: 0.272\n",
      "new_plans: 25 hv: 0.282\n",
      "new_plans: 30 hv: 0.276\n"
     ]
    }
   ],
   "source": [
    "for new_plans in new_plans_all:\n",
    "    path='.//TESTOUTPUT//real_case//greedy_new//max_sensor_100' + '_Lmax_20_new_plans_'+str(new_plans)+'//0//final_solution.pkl'\n",
    "    with open(path, 'rb') as f:\n",
    "        result = pickle.load(f)\n",
    "    obj=solution_to_objective_values(result)\n",
    "    obj_norm=obj.copy()  # obj should be array\n",
    "    for i in range(2):\n",
    "        obj_norm[:,i]=normalize(obj[:,i], norm_params, ['coverage', 'resolution'][i])\n",
    "    hv=pg.hypervolume(obj_norm).compute(ref_point)\n",
    "    print('new_plans:', new_plans, 'hv:', round(hv,3))\n",
    "    # print('HV for new greedy with {} new plans generated and network size of {} is {}.'.format(new_plans, size,round(hv,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcf566b",
   "metadata": {},
   "source": [
    "#### pareto front"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e973c4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "path='.//TESTOUTPUT//real_case//greedy_new//max_sensor_100_Lmax_20_new_plans_25//0//final_solution.pkl'\n",
    "with open(path,'rb') as f:\n",
    "    data=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2987a1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj=solution_to_objective_values(data)\n",
    "obj_df=pd.DataFrame(obj,columns=['coverage','resolution'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8ac16a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obj保存为csv，在csv中画拟合的曲线\n",
    "# path='.//post-analysis-result//real_case_solution.csv'\n",
    "# obj_df.to_csv(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8586b8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图，obj第一列为x-axis，第二列为y-axis，画scatter plot\n",
    "# 加一条虚线，y=0.0004x+3.9459, R2=0.9371\n",
    "fig, ax = plt.subplots(figsize=(5, 4), dpi=600)\n",
    "ax.plot(-obj[:,0], obj[:,1], 'bo', alpha=.9, markersize=1.5)\n",
    "#ax.plot([0.0004, 0.0016], [3.9459, 5.5], 'r--', linewidth=0.5)\n",
    "x=np.linspace(1000, 4000,100)\n",
    "ax.plot(x, 0.0004*x+3.9459, 'r--', linewidth=0.5)\n",
    "# 写上拟合的直线方程和R2\n",
    "ax.text(0.001, 6, '$y=0.0004x+3.9459$', fontname='Times New Roman', fontsize=8)\n",
    "ax.text(0.001, 5.5, '$R^2=0.9371$', fontname='Times New Roman', fontsize=8)\n",
    "ax.set_xlabel('Sensing Coverage', fontname='Times New Roman', fontsize=10)\n",
    "ax.set_ylabel('Expected Search Cost', fontname='Times New Roman', fontsize=10)\n",
    "plt.xticks(fontname='Times New Roman', fontsize=9)\n",
    "plt.yticks(fontname='Times New Roman', fontsize=9)\n",
    "plt.grid(False)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b921d8ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nuw7aYoJqeJhDs0alSlKtsFLzftaoVNZTqQfrqAAAgoKd1ykZkZaoeeP616ovwSb12ZnDGGN93PSRy+WS0+lUeXm54uLirC4HAGCRmnVKTr2g1fRRzBvX3xZhgJVpT2jM9ZseFQBAQDvTOiUOnVinZGhqguWhIDzMofTu7SytIdAwRwUAENBYpyS4EVQAAAGNdUqCG0EFABDQWKckuBFUAAABjXVKghtBBQAQ0FinJLgRVAAAAa9mnZIEp/fwToIz2ja3JsM33J4MADgrdlkbZERaooamJtiiFjQdggoAwGd2Ww2WdUqCD0M/AGADdnxGzZnUrAZ76homxeWVmrRoi3LyiyyqDMGEHhUAsJjdeiUaIpBWg0Vgo0cFACwUqL0SrAYLfyGoAIBFztQrIZ3olbDjMBCrwcJfLA0qs2fPlsPh8HolJCRYWRIA+E0g90qwGiz8xfI5KhdccIH++c9/et6Hh4dbWA0A+E8g90rUrAZbXF5ZZ4+QQyfWMGE1WJwty4d+WrRooYSEBM+rQ4cOVpcEAH4RyL0SrAYLf7E8qHz55ZdKSkpSSkqKbrnlFu3evbvetlVVVXK5XF4vALCar7cWB/ozalgNFv7gMMZYNktr1apVOnLkiM477zx99913evjhh/X5559r+/btateu9oI9s2fPVlZWVq3t5eXliouL80fJAM7ALquU+svZ3lpcc9ePJK8hlJq/WCBc8EPtnOPsuVwuOZ3OBl2/LQ0qpzp8+LC6d++u3/3ud5oxY0at/VVVVaqqqvK8d7lcSk5OJqgANhGI64GcjZqQceo/oo0NGaH2dwMaE1Qsn0x7slatWqlPnz768ssv69wfFRWlqKgoP1cFoCHqu2jXrAcSCD0DjdGUC57xjBqgfpbPUTlZVVWVduzYocTE4PnHDAgFgbweiK+a+tbimmfUjOnXSend2xFSgP9naVD57W9/qzVr1qiwsFCffPKJfv7zn8vlcmnChAlWlgWgkQJ5PRBfBfKtxUAgsXTo55tvvtGtt96qAwcOqEOHDrrkkku0ceNGdenSxcqyADRSKF60A/nWYiCQWBpUFi9ebOXXA2gioXjRZsEzwD9sNUcFQGAK9PVAfMGCZ4B/EFQAnLVQvWiz4BnQ/Gy1jkpjNeY+bADNL1TXA2HBM6BxAnbBt8YiqAD2w0UbwJkE7IJvAAJfzXogANAUmKMCAABsix4VIAQxPAMgUBBUgBATqhNeAQQmhn6AEFLz4MBTl7uveXBgTn6RRZUBQN0IKkCICMUHBwIIfAQVIESE4oMDAQQ+ggoQIkLxwYEAAh9BBQgRofjgQACBj6AChIhQfHAggMBHUAFCRKg+OBBAYCOoACGEp/0CCDQs+AaEmBFpiRqamsDKtAACAkEFCFBnsww+Dw4EECgIKgh5gfjcG5bBBxAqCCoIaYF4wa9ZBv/U9WNrlsFnrgmAYMJkWoSsQHzuDcvgAwg1BBWEpEC94LMMPoBQQ1BBSArUCz7L4AMINQQVhKRAveCzDD6AUENQQUgK1As+y+ADCDUEFYSkQL3gsww+gFBDUEFICuQLPsvgAwglDmOMvW5raASXyyWn06ny8nLFxcVZXQ4CUCCuo1IjEBeqAwCpcddvggpCHhd8APCvxly/WZkWIY/n3gCAfTFHBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BZBBQAA2BbrqMD2WJANAEIXQQW2FshL3AMAzh5DP7CtnPwiTVq0xSukSFJxeaUmLdqinPwiiyoDAPiLbYJKdna2HA6Hpk+fbnUpsIFqt1HWigLV9SCqmm1ZKwpU7Q7YR1UBABrAFkHl008/1QsvvKC+fftaXQpsIq+wtFZPysmMpKLySuUVlvqvKACA31keVH744QdlZGRowYIFatu2rdXlwCZKKuoPKb60AwAEJsuDSmZmpq699lpdc801Z2xbVVUll8vl9UJw6hgb3aTtAACBydK7fhYvXqzNmzdr06ZNDWqfnZ2trKysZq4KdjAoJV6JzmgVl1fWOU/FISnBeeJWZQBA8LKsR2Xfvn2aNm2aXn/9dUVHN+z/K545c6bKy8s9r3379jVzlbBKeJhDs0alSjoRSk5W837WqFTWUwGAIOcwxlhy28Rbb72l66+/XuHh4Z5t1dXVcjgcCgsLU1VVlde+urhcLjmdTpWXlysuLq65S4YFWEcFAIJPY67flg39XH311dq2bZvXtl/96lfq1auX7rvvvjOGFISGEWmJGpqawMq0ABCiLAsqsbGxSktL89rWqlUrtWvXrtZ2hLbwMIfSu7ezugwAgAV8DiplZWXKy8tTSUmJ3G63175f/OIXZ10YAACAT3NUVqxYoYyMDB0+fFixsbFyOP7TDe9wOFRa6p9FuJijAgBA4GnM9dunu37uuece3X777aqoqFBZWZkOHTrkefkrpAAAgODnU1D59ttvNXXqVLVs2bKp6wEAAPDwKagMHz68wYu0AdVuow27Durtrd9qw66DPEgQANBgPk2mvfbaa3XvvfeqoKBAffr0UUREhNf+0aNHN0lxCHysgwIAOBs+TaYNC6u/I8bhcKi6uvqsimooJtPaW05+kSYt2lJrCfyaqdfzxvUnrABACGr2ybRut7vel79CCuyt2m2UtaKgzuf01GzLWlHAMBAA4LQsf3oyglNeYanXcM+pjKSi8krlFXKXGACgfj4HlTVr1mjUqFHq0aOHevbsqdGjR2vdunVNWRsCWElF/SHFl3YAgNDkU1BZtGiRrrnmGrVs2VJTp07V5MmTFRMTo6uvvlpvvPFGU9eIANQxtmFPxG5oOwBAaPJpMm3v3r31X//1X7r77ru9ts+dO1cLFizQjh07mqzA02EyrX1Vu40uf/RDFZdX1jlPxSEpwRmtj++7igcMAkCIafbJtLt379aoUaNqbR89erQKCwt9OSSCTHiYQ7NGpUr6z10+NWrezxqVSkgBAJyWT0ElOTlZH3zwQa3tH3zwgZKTk8+6KASHEWmJmjeuvxKc3sM7Cc5obk0GADSITwu+3XPPPZo6daq2bt2qSy+9VA6HQx9//LFefvllPfnkk01dIwLYiLREDU1NUF5hqUoqKtUxNlqDUuLpSQEANIhPQWXSpElKSEjQY489pr///e+STsxbWbJkicaMGdOkBSLwhYc5lN69ndVlAAACkE+Tae2CybQAAASeZp9MCwAA4A8NHvqJj4/XF198ofbt26tt27ZyOOqfY1BaymqjAADg7DU4qDz++OOKjY31/OfTBRUAAICmwBwVAADgV80+RyU8PFwlJSW1th88eFDh4eG+HBIAAKAWn4JKfZ0wVVVVioyMPKuCAAAAajRqHZWnnnpKkuRwOPTiiy+qdevWnn3V1dVau3atevXq1bQVAgCAkNWooPL4449LOtGj8vzzz3sN80RGRqpr1656/vnnm7ZCAAAQshoVVGoeODhkyBAtW7ZMbdu2bZaiAAAAJB+X0F+9enVT1wEAAFCLT0Hl9ttvP+3+l156yadiAAAATuZTUDl06JDX+2PHjik/P19lZWW66qqrmqQw2Fe12/A0ZACAX/gUVJYvX15rm9vt1l133aVu3bqddVGwr5z8ImWtKFBReaVnW6IzWrNGpWpEWqKFlQEAglGTrky7c+dOXXnllSoqKmqqQ56W3VemDbaeh5z8Ik1atEWn/hem5hfNG9efsAIAOKPGXL996lGpz65du3T8+PGmPGTACraeh2q3UdaKglohRZKMToSVrBUFGpqaENBhDABgLz4FlRkzZni9N8aoqKhI7777riZMmNAkhQWy+noeissrNWnRloDsecgrLPUKXacykorKK5VXWKr07u38VxgAIKj5FFQ+++wzr/dhYWHq0KGDHnvssTPeERTsgrXnoaSi/pDiSzsAABqCdVSaWLD2PHSMjW7SdgAANIRPDyVE/YK152FQSrwSndGqrw/IoRNzcAalxPuzLABAkGtwj8pFF10kh6NhQxVbtmzxuaBAF6w9D+FhDs0alapJi7bIIXkNbdX8t2LWqNSAGs4CANhfg4PK2LFjm7GM4FHT81BcXlnnPBWHpIQA7XkYkZaoeeP617qbKSGA72YCANhbk66j4m92XUel5q4fqe6eh0C86+dkwbY+DADAvxpz/T6roLJ582bt2LFDDodDqampuuiii3w9lE/sGlSk4FtHBQCAptLsC76VlJTolltu0UcffaQ2bdrIGKPy8nINGTJEixcvVocOHXwqPJiMSEvU0NQEeh4AADgLPt31M2XKFLlcLm3fvl2lpaU6dOiQ8vPz5XK5NHXq1KauMWCFhzmU3r2dxvTrpPTu7QgpAAA0kk9BJScnR/PmzVPv3r0921JTU/Xss89q1apVDT7OvHnz1LdvX8XFxSkuLk7p6emN+jwAAAhuPgUVt9utiIiIWtsjIiLkdrsbfJzOnTvrkUce0aZNm7Rp0yZdddVVGjNmjLZv3+5LWQAAIMj4NJl2zJgxKisr05tvvqmkpCRJ0rfffquMjAy1bdtWy5cv97mg+Ph4/eUvf9Gvf/3rM7a182RaAABQt8Zcv33qUXnmmWdUUVGhrl27qnv37urRo4dSUlJUUVGhp59+2qeiq6urtXjxYh0+fFjp6ek+HQMAAAQXn+76SU5O1pYtW5Sbm6vPP/9cxhilpqbqmmuuafSxtm3bpvT0dFVWVqp169Zavny5UlNT62xbVVWlqqoqz3uXy+VL+QAAIEA02YJvZWVlatOmTaM/d/ToUe3du1dlZWVaunSpXnzxRa1Zs6bOsDJ79mxlZWXV2s7QDwAAgaPZF3x79NFH1bVrV918882SpJtuuklLly5VQkKCVq5cqQsvvNC3yiVdc8016t69u+bPn19rX109KsnJyQQVAAACSLPPUZk/f76Sk5MlSbm5ucrNzdWqVas0cuRI3Xvvvb4c0sMY4xVGThYVFeW5lbnmBQAAgpdPc1SKioo8QeUf//iHbrrpJg0bNkxdu3bVT37ykwYf54EHHtDIkSOVnJysiooKLV68WB999JFycnJ8KQsAAAQZn3pU2rZtq3379kk6sfhbzSRaY4yqq6sbfJzvvvtO48eP1/nnn6+rr75an3zyiXJycjR06FBfygIAAEHGpx6VG264Qbfddpt69uypgwcPauTIkZKkrVu3qkePHg0+zt/+9jdfvh4AAIQIn4LK448/rq5du2rfvn2aM2eOWrduLenEkNBdd93VpAUCAIDQ1WS3J1uBlWkBAAg8zX7XjyS99tpruvzyy5WUlKQ9e/ZIkp544gm9/fbbvh4SAADAi09BZd68eZoxY4ZGjhypsrIyzwTaNm3a6IknnmjK+gAAQAjzKag8/fTTWrBggX7/+98rPDzcs33gwIHatm1bkxVnZ9Vuow27Durtrd9qw66DqnYH7AgaAAC25dNk2sLCQl100UW1tkdFRenw4cNnXZTd5eQXKWtFgYrKKz3bEp3RmjUqVSPSEi2sDACA4OJTj0pKSoq2bt1aa/uqVavUu3fvs63J1nLyizRp0RavkCJJxeWVmrRoi3LyiyyqDACA4ONTj8q9996rzMxMVVZWyhijvLw8vfnmm/rzn/8c1GujVLuNslYUqK5BHiPJISlrRYGGpiYoPMzh5+oAAAg+PgWVX/3qVzp+/Lh+97vf6ciRI7rtttvUqVMnPf3007riiiuaukbbyCssrdWTcjIjqai8UnmFpUrv3s5/hQEAEKR8vj35zjvv1J49e1RSUqLi4mLl5eXps88+a9TKtIGmpKL+kOJLOwAAcHqNCiplZWXKyMhQhw4dlJSUpKeeekrx8fF69tln1aNHD23cuFEvvfRSc9VquY6x0U3aDgAAnF6jhn4eeOABrV27VhMmTFBOTo7uvvtu5eTkqLKyUitXrtTgwYObq05bGJQSr0RntIrLK+ucp+KQlOCM1qCUeH+XBgBAUGpUj8q7776rhQsX6q9//aveeecdGWN03nnn6cMPPwz6kCJJ4WEOzRqVKulEKDlZzftZo1KZSAsAQBNpVFDZv3+/UlNPXKi7deum6Oho3XHHHc1SmF2NSEvUvHH9leD0Ht5JcEZr3rj+rKMCAEATatTQj9vtVkREhOd9eHi4WrVq1eRF2d2ItEQNTU1QXmGpSioq1TH2xHAPPSkAADStRgUVY4x++ctfKioqSpJUWVmpiRMn1gory5Yta7oKbSo8zMEtyAAANLNGBZUJEyZ4vR83blyTFgMAAHCyRgWVhQsXNlcdAAAAtfi84BsAAEBzI6gAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbIqgAAADbsjSoZGdn6+KLL1ZsbKw6duyosWPHaufOnVaWBAAAbMTSoLJmzRplZmZq48aNys3N1fHjxzVs2DAdPnzYyrIAAIBNOIwxxuoianz//ffq2LGj1qxZo5/+9KdnbO9yueR0OlVeXq64uDg/VAgAAM5WY67fLfxUU4OUl5dLkuLj4+vcX1VVpaqqKs97l8vll7oAAIA1bDOZ1hijGTNm6PLLL1daWlqdbbKzs+V0Oj2v5ORkP1cJAAD8yTZDP5mZmXr33Xf18ccfq3PnznW2qatHJTk5maEfAAACSMAN/UyZMkXvvPOO1q5dW29IkaSoqChFRUX5sTIAAGAlS4OKMUZTpkzR8uXL9dFHHyklJcXKcgAAgM1YGlQyMzP1xhtv6O2331ZsbKyKi4slSU6nUzExMVaWBgAAbMDSOSoOh6PO7QsXLtQvf/nLM36e25MBAAg8ATNHxSbzeAEAgE3Z5vZkAACAUxFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbRFUAACAbVkaVNauXatRo0YpKSlJDodDb731lpXlAAAAm7E0qBw+fFgXXnihnnnmGSvLAAAANtXCyi8fOXKkRo4caWUJAADAxpijAgAAbMvSHpXGqqqqUlVVlee9y+WysBoAANDcAqpHJTs7W06n0/NKTk62uiQAANCMAiqozJw5U+Xl5Z7Xvn37rC4JAAA0o4Aa+omKilJUVJTVZQAAAD+xNKj88MMP+uqrrzzvCwsLtXXrVsXHx+vcc8+1sDIAAGAHlgaVTZs2aciQIZ73M2bMkCRNmDBBL7/8skVVAQAAu7A0qFx55ZUyxlhZAgAAsLGAmkwLAABCC0EFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYFkEFAADYVgurC7Cjo8fdem3D19pTekRd4ltqfHpXRbYg0wEA4G+WX32fe+45paSkKDo6WgMGDNC6dessrSd7ZYF6/XGVHnp3h17dsEcPvbtDvf64StkrCyytCwCAUGRpUFmyZImmT5+u3//+9/rss890xRVXaOTIkdq7d68l9WSvLND8tYVyG+/tbiPNX1tIWAEAwM8sDSpz587Vr3/9a91xxx3q3bu3nnjiCSUnJ2vevHl+r+XocbcWrCs8bZsF6wp19LjbTxUBAADLgsrRo0e1efNmDRs2zGv7sGHDtH79+jo/U1VVJZfL5fVqKq9t+LpWT8qp3OZEOwAA4B+WBZUDBw6ourpa55xzjtf2c845R8XFxXV+Jjs7W06n0/NKTk5usnr2lB5p0nYAAODsWT6Z1uFweL03xtTaVmPmzJkqLy/3vPbt29dkdXSJb9mk7QAAwNmzLKi0b99e4eHhtXpPSkpKavWy1IiKilJcXJzXq6mMT++qsLrzkUeY40Q7AADgH5YFlcjISA0YMEC5uble23Nzc3XppZf6v54WYbrzipTTtrnzihTWUwEAwI8sXfBtxowZGj9+vAYOHKj09HS98MIL2rt3ryZOnGhJPTN/lirpxN09J0+sDXOcCCk1+wEAgH9YGlRuvvlmHTx4UH/6059UVFSktLQ0rVy5Ul26dLGsppk/S9U9w3qxMi0AADbgMMac4aZc+3K5XHI6nSovL2/S+SoAAKD5NOb6TTcBAACwLYIKAACwLYIKAACwLYIKAACwLYIKAACwLYIKAACwLYIKAACwLYIKAACwLYIKAACwLUuX0D9bNYvqulwuiysBAAANVXPdbsji+AEdVCoqKiRJycnJFlcCAAAaq6KiQk6n87RtAvpZP263W/v371dsbKwcDscZ27tcLiUnJ2vfvn08GyiAcN4CD+csMHHeAlMgnjdjjCoqKpSUlKSwsNPPQgnoHpWwsDB17ty50Z+Li4sLmJOJ/+C8BR7OWWDivAWmQDtvZ+pJqcFkWgAAYFsEFQAAYFshFVSioqI0a9YsRUVFWV0KGoHzFng4Z4GJ8xaYgv28BfRkWgAAENxCqkcFAAAEFoIKAACwLYIKAACwLYIKAACwrZAJKs8995xSUlIUHR2tAQMGaN26dVaXFDLWrl2rUaNGKSkpSQ6HQ2+99ZbXfmOMZs+eraSkJMXExOjKK6/U9u3bvdpUVVVpypQpat++vVq1aqXRo0frm2++8Wpz6NAhjR8/Xk6nU06nU+PHj1dZWVkz/7rglZ2drYsvvlixsbHq2LGjxo4dq507d3q14dzZz7x589S3b1/P4l/p6elatWqVZz/nzP6ys7PlcDg0ffp0z7aQPm8mBCxevNhERESYBQsWmIKCAjNt2jTTqlUrs2fPHqtLCwkrV640v//9783SpUuNJLN8+XKv/Y888oiJjY01S5cuNdu2bTM333yzSUxMNC6Xy9Nm4sSJplOnTiY3N9ds2bLFDBkyxFx44YXm+PHjnjYjRowwaWlpZv369Wb9+vUmLS3NXHfddf76mUFn+PDhZuHChSY/P99s3brVXHvttebcc881P/zwg6cN585+3nnnHfPuu++anTt3mp07d5oHHnjAREREmPz8fGMM58zu8vLyTNeuXU3fvn3NtGnTPNtD+byFRFAZNGiQmThxote2Xr16mfvvv9+iikLXqUHF7XabhIQE88gjj3i2VVZWGqfTaZ5//nljjDFlZWUmIiLCLF682NPm22+/NWFhYSYnJ8cYY0xBQYGRZDZu3Ohps2HDBiPJfP755838q0JDSUmJkWTWrFljjOHcBZK2bduaF198kXNmcxUVFaZnz54mNzfXDB482BNUQv28Bf3Qz9GjR7V582YNGzbMa/uwYcO0fv16i6pCjcLCQhUXF3udn6ioKA0ePNhzfjZv3qxjx455tUlKSlJaWpqnzYYNG+R0OvWTn/zE0+aSSy6R0+nkPDeR8vJySVJ8fLwkzl0gqK6u1uLFi3X48GGlp6dzzmwuMzNT1157ra655hqv7aF+3gL6oYQNceDAAVVXV+ucc87x2n7OOeeouLjYoqpQo+Yc1HV+9uzZ42kTGRmptm3b1mpT8/ni4mJ17Nix1vE7duzIeW4CxhjNmDFDl19+udLS0iRx7uxs27ZtSk9PV2VlpVq3bq3ly5crNTXVczHinNnP4sWLtXnzZm3atKnWvlD/31rQB5UaDofD670xptY2WMeX83Nqm7rac56bxuTJk/Xvf/9bH3/8ca19nDv7Of/887V161aVlZVp6dKlmjBhgtasWePZzzmzl3379mnatGl6//33FR0dXW+7UD1vQT/00759e4WHh9dKiyUlJbXSKfwvISFBkk57fhISEnT06FEdOnTotG2+++67Wsf//vvvOc9nacqUKXrnnXe0evVqde7c2bOdc2dfkZGR6tGjhwYOHKjs7GxdeOGFevLJJzlnNrV582aVlJRowIABatGihVq0aKE1a9boqaeeUosWLTx/01A9b0EfVCIjIzVgwADl5uZ6bc/NzdWll15qUVWokZKSooSEBK/zc/ToUa1Zs8ZzfgYMGKCIiAivNkVFRcrPz/e0SU9PV3l5ufLy8jxtPvnkE5WXl3OefWSM0eTJk7Vs2TJ9+OGHSklJ8drPuQscxhhVVVVxzmzq6quv1rZt27R161bPa+DAgcrIyNDWrVvVrVu30D5v/p+/6381tyf/7W9/MwUFBWb69OmmVatW5uuvv7a6tJBQUVFhPvvsM/PZZ58ZSWbu3Lnms88+89we/sgjjxin02mWLVtmtm3bZm699dY6b7vr3Lmz+ec//2m2bNlirrrqqjpvu+vbt6/ZsGGD2bBhg+nTp4/tb7uzs0mTJhmn02k++ugjU1RU5HkdOXLE04ZzZz8zZ840a9euNYWFhebf//63eeCBB0xYWJh5//33jTGcs0Bx8l0/xoT2eQuJoGKMMc8++6zp0qWLiYyMNP379/fcYonmt3r1aiOp1mvChAnGmBO33s2aNcskJCSYqKgo89Of/tRs27bN6xg//vijmTx5somPjzcxMTHmuuuuM3v37vVqc/DgQZORkWFiY2NNbGysycjIMIcOHfLTrww+dZ0zSWbhwoWeNpw7+7n99ts9/9Z16NDBXH311Z6QYgznLFCcGlRC+bw5jDHGmr4cAACA0wv6OSoAACBwEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAnFFxcbGmTJmibt26KSoqSsnJyRo1apQ++OADq0sDEORaWF0AAHv7+uuvddlll6lNmzaaM2eO+vbtq2PHjum9995TZmamPv/8c0vqOnr0qCIjIy35bgD+Q48KgNO666675HA4lJeXp5///Oc677zzdMEFF2jGjBnauHGjJGnv3r0aM2aMWrdurbi4ON10002ex8nv3LlTDoejVqCZO3euunbtqpqneBQUFOhnP/uZWrdurXPOOUfjx4/XgQMHPO2vvPJKTZ48WTNmzFD79u01dOhQz3H69OmjVq1aKTk5WXfddZd++OEHr+9asGCBkpOT1bJlS11//fWaO3eu2rRp49VmxYoVGjBggKKjo9WtWzdlZWXp+PHjTfq3BNB4BBUA9SotLVVOTo4yMzPVqlWrWvvbtGkjY4zGjh2r0tJSrVmzRrm5udq1a5duvvlmSdL555+vAQMG6PXXX/f67BtvvKHbbrtNDodDRUVFGjx4sPr166dNmzYpJydH3333nW666Savz7zyyitq0aKF/vWvf2n+/PmSpLCwMD311FPKz8/XK6+8og8//FC/+93vPJ/517/+pYkTJ2ratGnaunWrhg4dqv/+7//2Ou57772ncePGaerUqSooKND8+fP18ssv12oHwALWPhMRgJ198sknRpJZtmxZvW3ef/99Ex4e7vWU1u3btxtJJi8vzxhjzNy5c023bt08+3fu3Gkkme3btxtjjPnjH/9ohg0b5nXcffv2GUlm586dxpgTT5Pt16/fGWv++9//btq1a+d5f/PNN5trr73Wq01GRoZxOp2e91dccYX585//7NXmtddeM4mJiWf8PgDNix4VAPUy/z8s43A46m2zY8cOJScnKzk52bMtNTVVbdq00Y4dOyRJt9xyi/bs2eMZKnr99dfVr18/paamSpI2b96s1atXq3Xr1p5Xr169JEm7du3yHHfgwIG1vn/16tUaOnSoOnXqpNjYWP3iF7/QwYMHdfjwYUknhp4GDRrk9ZlT32/evFl/+tOfvL7/zjvvVFFRkY4cOdKwPxaAZsFkWgD16tmzpxwOh3bs2KGxY8fW2cYYU2eQOXl7YmKihgwZojfeeEOXXHKJ3nzzTf3mN7/xtHW73Ro1apQeffTRWsdJTEz0/OdTh5/27Nmjn/3sZ5o4caIeeughxcfH6+OPP9avf/1rHTt2rN76agLYyd+flZWlG264odb3R0dH1/m7AfgHQQVAveLj4zV8+HA9++yzmjp1aq2gUFZWptTUVO3du1f79u3z9KoUFBSovLxcvXv39rTNyMjQfffdp1tvvVW7du3SLbfc4tnXv39/LV26VF27dlWLFg3/Z2nTpk06fvy4HnvsMYWFnegg/vvf/+7VplevXsrLy6v1uZP1799fO3fuVI8ePRr83QD8g6EfAKf13HPPqbq6WoMGDdLSpUv15ZdfaseOHXrqqaeUnp6ua665Rn379lVGRoa2bNmivLw8/eIXv9DgwYO9hmpuuOEGuVwuTZo0SUOGDFGnTp08+zIzM1VaWqpbb71VeXl52r17t95//33dfvvtqq6urre27t276/jx43r66ae1e/duvfbaa3r++ee92kyZMkUrV67U3Llz9eWXX2r+/PlatWqVVy/Lgw8+qFdffVWzZ8/W9u3btWPHDi1ZskR/+MMfmvAvCcAnls6QARAQ9u/fbzIzM02XLl1MZGSk6dSpkxk9erRZvXq1McaYPXv2mNGjR5tWrVqZ2NhYc+ONN5ri4uJax7nxxhuNJPPSSy/V2vfFF1+Y66+/3rRp08bExMSYXr16menTpxu3222MOTGZdtq0abU+N3fuXJOYmGhiYmLM8OHDzauvvmokmUOHDnnavPDCC6ZTp04mJibGjB071jz88MMmISHB6zg5OTnm0ksvNTExMSYuLs4MGjTIvPDCC77/0QA0CYcxpwzWAkCQu/POO/X5559r3bp1VpcC4AyYowIg6P31r3/V0KFD1apVK61atUqvvPKKnnvuOavLAtAA9KgACHo33XSTPvroI1VUVKhbt26aMmWKJk6caHVZABqAoAIAAGyLu34AAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBtEVQAAIBt/R8TYlu/41jfsgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cost-effective plot, x-axis is coverage, y-axis is resolution\n",
    "plt.scatter(-obj_df['coverage'], obj_df['resolution'])\n",
    "plt.xlabel('Coverage')\n",
    "plt.ylabel('Resolution')\n",
    "plt.title('Scatter Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbfe9f2c",
   "metadata": {},
   "source": [
    "#### select one solution：coverage最大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be16e67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "coverage=int(obj_df['coverage'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e358a9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ind in data:\n",
    "    if ind[1]==coverage:\n",
    "        sol_example=ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6a76a351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100,\n",
       " -4204,\n",
       " 6.537459549227607,\n",
       " [20,\n",
       "  37,\n",
       "  57,\n",
       "  92,\n",
       "  100,\n",
       "  108,\n",
       "  160,\n",
       "  230,\n",
       "  252,\n",
       "  279,\n",
       "  281,\n",
       "  357,\n",
       "  383,\n",
       "  413,\n",
       "  464,\n",
       "  617,\n",
       "  715,\n",
       "  848,\n",
       "  950,\n",
       "  1137,\n",
       "  1143,\n",
       "  1149,\n",
       "  1171,\n",
       "  1237,\n",
       "  1245,\n",
       "  1265,\n",
       "  1299,\n",
       "  1348,\n",
       "  1364,\n",
       "  1389,\n",
       "  1455,\n",
       "  1475,\n",
       "  1510,\n",
       "  1635,\n",
       "  1636,\n",
       "  1831,\n",
       "  1956,\n",
       "  2015,\n",
       "  2016,\n",
       "  2078,\n",
       "  2083,\n",
       "  2129,\n",
       "  2187,\n",
       "  2197,\n",
       "  2312,\n",
       "  2360,\n",
       "  2368,\n",
       "  2393,\n",
       "  2464,\n",
       "  2491,\n",
       "  2527,\n",
       "  2546,\n",
       "  2600,\n",
       "  2607,\n",
       "  2649,\n",
       "  2732,\n",
       "  2749,\n",
       "  2830,\n",
       "  2834,\n",
       "  2841,\n",
       "  2845,\n",
       "  2851,\n",
       "  2852,\n",
       "  2884,\n",
       "  2887,\n",
       "  3041,\n",
       "  3092,\n",
       "  3161,\n",
       "  3180,\n",
       "  3215,\n",
       "  3242,\n",
       "  3341,\n",
       "  3358,\n",
       "  3367,\n",
       "  3371,\n",
       "  3382,\n",
       "  3389,\n",
       "  3391,\n",
       "  3528,\n",
       "  3595,\n",
       "  3651,\n",
       "  3671,\n",
       "  3681,\n",
       "  3719,\n",
       "  3737,\n",
       "  3742,\n",
       "  3770,\n",
       "  3809,\n",
       "  3852,\n",
       "  3949,\n",
       "  3996,\n",
       "  4005,\n",
       "  4073,\n",
       "  4193,\n",
       "  4224,\n",
       "  4248,\n",
       "  4325,\n",
       "  4327,\n",
       "  4340,\n",
       "  4347]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef1fbbc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取安置sensor的manhole\n",
    "# 所有manhole的node\n",
    "manhole=list(range(0, len(upstream_set)))\n",
    "# 所有manhole标记都为0\n",
    "mark=[0]*len(upstream_set)\n",
    "\n",
    "# 提取出此solution中放置了sensor的manhole\n",
    "sensor=sol_example[3]\n",
    "# 对于被放置sensor的manhole，标记变为1\n",
    "for node in sensor:\n",
    "    mark[node]=1\n",
    "\n",
    "# 将node与其标记连起来，形成一个dictionary\n",
    "sensor_dictionary =  dict(zip(manhole, mark))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5e0ae21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取被cover的manhole，记录每个node被cover的次数\n",
    "covered_node=[]\n",
    "covered_times=[0]*len(upstream_set)\n",
    "for node in sensor:\n",
    "    covered_set=upstream_set[node]\n",
    "    for j in covered_set:\n",
    "        if j in covered_node:\n",
    "            covered_times[j]+=1\n",
    "        else:\n",
    "            covered_times[j]=1\n",
    "            covered_node.append(j)\n",
    "# 将node与它被cover的次数连起来，形成一个dictionary\n",
    "covered_node_dictionary =  dict(zip(manhole, covered_times))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aaf5a1e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 给每个node加上是否被cover、被cover的次数、以及坐标\n",
    "nx.set_node_attributes(relabeled_G,sensor_dictionary, \"whether-selected\")\n",
    "nx.set_node_attributes(relabeled_G,covered_node_dictionary,'covered_times')\n",
    "nx.set_node_attributes(relabeled_G,pos_data, \"coordinate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "65492675",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将node、选中次数、经纬度分别读取，成为list\n",
    "node=[]\n",
    "whether_covered=[]\n",
    "covered_times=[]\n",
    "coordinate=[]\n",
    "for i in range(4394):\n",
    "    node.append(i)\n",
    "    whether_covered.append(relabeled_G.nodes[i]['whether-selected'])\n",
    "    covered_times.append(relabeled_G.nodes[i]['covered_times'])\n",
    "    coordinate.append(relabeled_G.nodes[i]['coordinate'])\n",
    "    #print(relabeled_G.nodes[i])\n",
    "\n",
    "# list转化为dataframe\n",
    "node=pd.DataFrame(node,columns=['node'])\n",
    "whether_covered=pd.DataFrame(whether_covered,columns=['whether_selected'])\n",
    "covered_times=pd.DataFrame(covered_times,columns=['covered_times'])\n",
    "coordinate=pd.DataFrame(coordinate,columns=['lon','lat'])\n",
    "\n",
    "# 合并dataframe\n",
    "final=pd.concat([node,whether_covered,covered_times,coordinate],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c595798",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_41520\\2174046363.py:4: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  gdf.to_file('./post-analysis-result/example-viz-data-new/node-max-coverage.shp')\n"
     ]
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame(\n",
    "    final, geometry=gpd.points_from_xy(final.lon,final.lat))\n",
    "gdf.set_crs('epsg:2326')\n",
    "gdf.to_file('./post-analysis-result/example-viz-data-new/node-max-coverage.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d37188d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取被cover的edge以及每条edge被cover的次数\n",
    "covered_edge={}\n",
    "for node in sensor:\n",
    "    covered_link_set=upstream_links_set[node] # 提取sensor上游的link\n",
    "    for j in covered_link_set:  # 对cover的link遍历\n",
    "        if j in covered_edge.keys():   # 已经存在，value+1\n",
    "            covered_edge[j]+=1\n",
    "        else:  # 不存在，加入且value=1\n",
    "            covered_edge[j]=1\n",
    "\n",
    "# 将边被cover的次数赋给每个edge\n",
    "nx.set_edge_attributes(relabeled_G,covered_edge,'covered_times')\n",
    "\n",
    "# 转化为dataframe\n",
    "test=nx.to_pandas_edgelist(relabeled_G)\n",
    "# 将没有被cover到的edge对应的covered_times设置为0\n",
    "test=test.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3f10f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_41520\\2819811131.py:8: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\n",
      "  gdf.to_file('./post-analysis-result/example-viz-data-new/edge-max-coverage.shp')\n"
     ]
    }
   ],
   "source": [
    "# 设置geometry\n",
    "test['geom'] = test['Wkt'].apply(wkt.loads)\n",
    "gdf = gpd.GeoDataFrame(test, geometry='geom')\n",
    "# 无关的列直接去掉\n",
    "gdf=gdf[['source','target','covered_times','geom']]\n",
    "# 设置坐标\n",
    "gdf=gdf.set_crs('epsg:2326')\n",
    "gdf.to_file('./post-analysis-result/example-viz-data-new/edge-max-coverage.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7915d6ae",
   "metadata": {},
   "source": [
    "# pre-analysis of London sewage pipes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e873a2f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-8bf985849a5a>:4: DeprecationWarning: read_shp is deprecated and will be removed in 3.0.See https://networkx.org/documentation/latest/auto_examples/index.html#geospatial.\n",
      "  G = nx.read_shp(path)\n"
     ]
    }
   ],
   "source": [
    "#path='D://project//data//London_sewage_pop//sewer_pipes.shp'\n",
    "path='.//DATA//real_life_case_network//data//pipe_TM.shp'\n",
    "# networkx读取数据\n",
    "G = nx.read_shp(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0aeebaf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_directed(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2a3be187",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-ed9cf5bce324>:1: DeprecationWarning: info is deprecated and will be removed in version 3.0.\n",
      "\n",
      "  nx.info(G)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'DiGraph with 4394 nodes and 4309 edges'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.info(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85b64a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_undirected=G.to_undirected()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2eec262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_connected(G_undirected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "895c0406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查多少个subgraph\n",
    "subgraphs = list(nx.connected_components(G_undirected))\n",
    "len(subgraphs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "694a4a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def HV_comparison(obj_new, obj_original):\n",
    "    obj_new_df = pd.DataFrame(obj_new, columns=['coverage', 'resolution'])\n",
    "    obj_original_df = pd.DataFrame(obj_original, columns=['coverage', 'resolution'])\n",
    "\n",
    "    def cal_norm_para(obj_new_df, obj_original_df):\n",
    "        min_max_df=pd.concat([obj_new_df, obj_original_df], ignore_index=True)\n",
    "        coverage_min=min_max_df['coverage'].min()\n",
    "        coverage_max=min_max_df['coverage'].max()\n",
    "        resolution_min=min_max_df['resolution'].min()\n",
    "        resolution_max=min_max_df['resolution'].max()\n",
    "        norm_params={'coverage':[coverage_min,coverage_max],\n",
    "               'resolution':[resolution_min,resolution_max]}\n",
    "        return norm_params\n",
    "\n",
    "    def normalize(values, norm_params, col):\n",
    "        mini, maxi = norm_params[col]\n",
    "        return (values - mini)/(maxi - mini)\n",
    "\n",
    "    norm_params=cal_norm_para(obj_new_df, obj_original_df)\n",
    "\n",
    "    def cal_HV(obj):\n",
    "        ref_point=[1,1]\n",
    "        obj_norm=obj.copy()  # obj should be array\n",
    "        for i in range(2):\n",
    "            obj_norm[:,i]=normalize(obj[:,i], norm_params, ['coverage', 'resolution'][i])\n",
    "        hv=pg.hypervolume(obj_norm).compute(ref_point)\n",
    "        return hv\n",
    "    \n",
    "    hv_new=cal_HV(obj_new)\n",
    "    hv_original=cal_HV(obj_original)\n",
    "    print('hv_new:', hv_new)\n",
    "    print('hv_original:', hv_original)\n",
    "\n",
    "    return hv_new, hv_original\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d20dcb5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5017e920",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='D://project//data//pipeTM_Adjust//pipeTM_Adjust//pipe_TM_Adjust_WGS84.shp'\n",
    "# use geopandas\n",
    "gdf = gpd.read_file(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b446360",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.to_crs('epsg:4326', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a960a66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "38261c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pop_path='D://project//data//SSBG_16BC_84//SSBG_16BC_84//SSBG_16BC_84.shp'\n",
    "pop_path='D://project//data//2016PopulationBycensusStatisticsBySmallStreetBlockGroup_SHP//SSBG_16BC.shp'\n",
    "pop = gpd.read_file(pop_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c9f74a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:2326>\n",
       "Name: Hong Kong 1980 Grid System\n",
       "Axis Info [cartesian]:\n",
       "- N[north]: Northing (metre)\n",
       "- E[east]: Easting (metre)\n",
       "Area of Use:\n",
       "- name: China - Hong Kong - onshore and offshore.\n",
       "- bounds: (113.76, 22.13, 114.51, 22.58)\n",
       "Coordinate Operation:\n",
       "- name: Hong Kong 1980 Grid\n",
       "- method: Transverse Mercator\n",
       "Datum: Hong Kong 1980\n",
       "- Ellipsoid: International 1924\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "51b1e127",
   "metadata": {},
   "outputs": [],
   "source": [
    "pop.to_crs('epsg:4326', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4599c2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World.\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984 ensemble\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9dd7427e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "gdf.plot(ax=ax, color='black', linewidth=0.5)\n",
    "pop.plot(ax=ax, color='red', markersize=1)\n",
    "plt.show()"
   ]
  }
 ],
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