建模算法/Dijkstra/question1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "186271c4",
   "metadata": {},
   "source": [
    "## 1.导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3dc2abc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import FancyArrowPatch\n",
    "from mpl_toolkits.mplot3d import proj3d\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']    # 指定默认字体：解决plot不能显示中文问题\n",
    "mpl.rcParams['axes.unicode_minus'] = False           # 解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8ffd38",
   "metadata": {},
   "source": [
    "## 2.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bde715d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\"D:\\Dijkstra\\附件1：数据集1-终稿.xlsx\",header=1,index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2715804e",
   "metadata": {},
   "source": [
    "## 3.数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7fa1898",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        X坐标（单位: m）     Y坐标（单位:m）   Z坐标（单位: m） 校正点类型  第三问点标记\n",
      "编号                                                         \n",
      "0         0.000000  50000.000000  5000.000000   A 点       0\n",
      "1     33070.825831   2789.480761  5163.524680     0       0\n",
      "2     54832.887019  49179.219108  1448.303791     1       1\n",
      "3     77991.545911  63982.175286  5945.823038     0       0\n",
      "4     16937.179535  84714.341903  5360.293002     0       0\n",
      "..             ...           ...          ...   ...     ...\n",
      "608   45789.201490  21191.207906   440.001872     1       0\n",
      "609   94917.734859  82958.725321  6169.657707     0       0\n",
      "610   14870.601682  95939.173362  8248.836185     0       0\n",
      "611   93009.567446   4549.333260  7882.608297     1       0\n",
      "612  100000.000000  59652.343380  5022.001164    B点       0\n",
      "\n",
      "[613 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "df1 = df\n",
    "print(df1)\n",
    "n = df1.shape[0]-1\n",
    "df1.loc[0, '校正点类型'] = 2  # A点用2表示\n",
    "df1.loc[n, '校正点类型'] = 3  # B点用3表示\n",
    "data = np.array(df1.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694e6acc",
   "metadata": {},
   "source": [
    "## 4.生成三维散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6fd0842",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_8220/2098187255.py:21: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.\n",
      "  ax = Axes3D(fig)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_3D_scatter(data):\n",
    "    #### 起点 ####\n",
    "    Xa = data[0][0]\n",
    "    Ya = data[0][1]\n",
    "    Za = data[0][2]\n",
    "    #### 终点 ####\n",
    "    Xb = data[n][0]\n",
    "    Yb = data[n][1]\n",
    "    Zb = data[n][2]\n",
    "    #### 水平校正点 ####\n",
    "    Xh = data[data[:,3]==0][:,0]\n",
    "    Yh = data[data[:,3]==0][:,1]\n",
    "    Zh = data[data[:,3]==0][:,2]\n",
    "    #### 垂直校正点 ####\n",
    "    Xv = data[data[:,3]==1][:,0]\n",
    "    Yv = data[data[:,3]==1][:,1]\n",
    "    Zv = data[data[:,3]==1][:,2]\n",
    "\n",
    "    #### 绘制三维散点图 ####\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    ax.scatter(Xa,Ya,Za, c='r', s=10, label='起点')\n",
    "    ax.scatter(Xb,Yb,Zb, c='g', s=10, label='终点')\n",
    "    ax.scatter(Xh,Yh,Zh, c='b', s=10, label='水平校正点')\n",
    "    ax.scatter(Xv,Yv,Zv, c='y', s=10, label='垂直校正点')\n",
    "\n",
    "    # 绘制图例\n",
    "    ax.legend(loc='best')\n",
    "\n",
    "    # 添加坐标轴(顺序是Z, Y, X)\n",
    "    ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'})\n",
    "    ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'})\n",
    "    ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'})\n",
    "\n",
    "    ax.view_init(elev=10., azim=-80)\n",
    "    # 展示\n",
    "    plt.title(\"三维散点图\")\n",
    "    plt.show()\n",
    "generate_3D_scatter(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dff905f",
   "metadata": {},
   "source": [
    "## 5.生成距离矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f15af663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., inf, inf, ..., inf, inf, inf],\n",
       "       [inf,  0., inf, ..., inf, inf, inf],\n",
       "       [inf, inf,  0., ..., inf, inf, inf],\n",
       "       ...,\n",
       "       [inf, inf, inf, ...,  0., inf, inf],\n",
       "       [inf, inf, inf, ..., inf,  0., inf],\n",
       "       [inf, inf, inf, ..., inf, inf,  0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 问题1：α1 = 25，α2 = 15，β1 = 20，β2 = 25，𝜃 = 30，𝛿 = 0.001\n",
    "##计算两点之间的欧式距离\n",
    "def cal_distance(point1,point2):\n",
    "    return np.linalg.norm(point1 - point2)\n",
    "##建立距离矩阵\n",
    "##根据第一问要求可以得到 当垂直-水平校正交替的情况下能够获得最优航迹路径；\n",
    "def build_distance_matrix(data, delta, limit):\n",
    "    n = len(data)\n",
    "    mat = np.zeros([n,n])\n",
    "    for i in range(0,n):\n",
    "        point1 = data[i,0:3]\n",
    "        attr1 = data[i,3]\n",
    "        for j in range(0,n):\n",
    "            if j != i:\n",
    "                point2 = data[j,0:3]\n",
    "                attr2 = data[j, 3]\n",
    "                dis = cal_distance(point1,point2)\n",
    "                #如果连续两个水平校正点或连续两个垂直校正点，则把两点之间的距离设为无穷大\n",
    "                if attr1==0 and attr2 ==0:\n",
    "                    mat[i][j] = float('inf')\n",
    "                elif attr1==1 and attr2==1:\n",
    "                    mat[i][j] = float('inf')\n",
    "                else:\n",
    "                    if dis * delta <= limit:\n",
    "                        mat[i][j] = dis\n",
    "                    #如果两点之间的误差大于α2，β1时无法进行任何校正\n",
    "                    else:\n",
    "                        mat[i][j] = float('inf')\n",
    "    return mat\n",
    "dis_mat = build_distance_matrix(data,0.001,20)\n",
    "dis_mat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb8e32cf",
   "metadata": {},
   "source": [
    "## 6.带约束的Dijkstra算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8d50bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "到达每个节点的最短路径列表: [0, 60321.910296795904, 60199.083837529884, 81680.71352407693, 40939.58199594708, 39994.805899071456, 26654.787182225842, 53378.74272468644, 103931.5251708937, 30886.763107429, 63440.74132057692, 60813.6068461543, 88227.17147782682, 36472.69561303324, 92327.76220088544, 40250.925136774094, 101083.83207066498, 89468.13396258504, 100950.37830044478, 22794.724738751705, 109925.24135330584, 105671.63003266303, 62810.826392372575, 64806.55924696947, 63209.990153740444, 51594.14555123179, 36728.734842267535, 53183.16390837809, 36058.856082831764, 17821.378276879175, 79182.09494423348, 57338.640382779384, 59903.74914428475, 50689.736610574706, 89677.72881126375, 74959.3574776092, 55378.018463549146, 41872.95955901542, 70470.74498267651, 94437.89864314374, 9350.105206995011, 87394.72114830452, 40904.45780674893, 94964.0280660708, 78205.95085060986, 57847.16254553407, 97417.09262014052, 29836.096996336, 93277.50311407167, 42775.389981435896, 31318.353423175125, 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      "========================================================================================================================\n",
      "前驱节点列表： [inf, 419, 306, 450, 367, 432, 125, 502, 575, 509, 586, 61, 214, 401, 406, 199, 266, 30, 397, 145, 8, 292, 111, 349, 345, 73, 429, 399, 506, 0, 94, 255, 70, 233, 30, 572, 148, 367, 63, 406, 0, 321, 82, 576, 251, 244, 436, 176, 30, 167, 591, 13, 7, 448, 395, 162, 607, 211, 606, 457, 592, 607, 255, 255, 578, 451, 125, 214, 211, 503, 42, 0, 251, 536, 136, 91, 200, 180, 404, 0, 354, 571, 298, 341, 339, 182, 442, 207, 580, 607, 162, 294, 200, 411, 191, 256, 312, 462, 579, 193, 398, 335, 446, 606, 515, 83, 579, 37, 80, 297, 559, 234, 365, 450, 125, 422, 273, 271, 349, 592, 470, 100, 33, 69, 571, 416, 451, 65, 163, 287, 454, 582, 297, 2, 592, 185, 294, 96, 579, 470, 491, 340, 155, 569, 335, 0, 13, 539, 15, 475, 160, 91, 557, 24, 606, 237, 14, 271, 8, 123, 327, 458, 279, 354, 296, 77, 206, 509, 41, 124, 607, 192, 369, 7, 491, 296, 346, 73, 429, 486, 0, 225, 101, 28, 144, 134, 543, 93, 22, 297, 451, 7, 571, 183, 183, 225, 251, 395, 338, 303, 0, 60, 0, 256, 230, 0, 338, 267, 460, 22, 503, 0, 536, 271, 369, 82, 549, 311, 123, 269, 30, 86, 86, 545, 510, 584, 458, 119, 471, 269, 83, 125, 510, 237, 55, 401, 525, 69, 365, 294, 530, 349, 58, 321, 215, 349, 378, 322, 450, 591, 61, 23, 223, 167, 302, 73, 596, 293, 30, 591, 152, 14, 450, 349, 608, 193, 483, 167, 317, 301, 407, 7, 153, 513, 79, 176, 483, 340, 170, 0, 287, 37, 91, 292, 96, 0, 450, 44, 193, 557, 274, 0, 551, 442, 503, 422, 606, 592, 71, 226, 428, 251, 397, 0, 182, 497, 148, 149, 191, 271, 358, 346, 466, 185, 483, 33, 343, 379, 255, 266, 395, 264, 191, 481, 223, 347, 480, 124, 581, 124, 367, 125, 390, 235, 572, 312, 234, 90, 155, 559, 457, 450, 128, 82, 180, 573, 0, 251, 558, 218, 422, 199, 586, 153, 200, 310, 551, 200, 376, 99, 325, 95, 580, 293, 277, 180, 294, 505, 286, 278, 436, 506, 263, 14, 242, 155, 591, 462, 152, 312, 579, 388, 182, 270, 356, 452, 296, 380, 457, 153, 4, 30, 125, 0, 191, 559, 292, 214, 255, 371, 214, 568, 273, 315, 66, 359, 113, 592, 255, 259, 583, 475, 147, 404, 60, 396, 0, 578, 371, 293, 121, 67, 405, 321, 556, 555, 4, 271, 365, 591, 328, 0, 452, 557, 96, 325, 555, 83, 327, 507, 351, 358, 112, 182, 12, 423, 545, 312, 450, 356, 194, 558, 180, 155, 22, 471, 223, 338, 44, 223, 112, 155, 244, 257, 508, 121, 215, 28, 244, 44, 376, 63, 372, 121, 233, 335, 543, 161, 395, 576, 192, 424, 558, 556, 0, 113, 559, 208, 322, 554, 454, 292, 0, 478, 83, 569, 199, 397, 409, 192, 481, 583, 90, 0, 257, 416, 69, 70, 83, 200, 378, 114, 177, 457, 8, 23, 132, 83, 12, 95, 2, 0, 198, 611, 75, 592, 559, 0, 14, 0, 555, 349, 356, 349, 82, 306, 57, 275, 312, 73, 607, 233, 201, 462, 13, 68, 255, 186, 30, 401, 320, 394, 365, 124, 395, 457, 198, 365, 76, 322, 397, 33, 82, 0, 169, 22, 166, 130, 211, 311, 422, 55, 127, 90, 301, 559, 515, 127, 0, 376, 117, 183, 395, 403, 288, 150, 289, 269, 124, 251, 275, 145, 606, 481, 450, 124, 163, 297, 233, 60, 68, 251, 269, 558, 405, 542, 482, 91, 553, 115, 579, 54, 397]\n",
      "========================================================================================================================\n",
      "到达每个点需要经过的节点个数: [0, 7, 6, 8, 4, 4, 3, 5, 10, 3, 6, 6, 9, 4, 10, 3, 10, 9, 10, 2, 11, 10, 6, 6, 6, 5, 4, 6, 4, 1, 8, 6, 6, 5, 9, 8, 5, 4, 7, 10, 1, 10, 4, 9, 8, 6, 9, 3, 9, 4, 3, 5, 6, 9, 10, 3, 5, 2, 6, 7, 7, 5, 6, 6, 2, 5, 3, 9, 2, 2, 5, 1, 8, 4, 4, 4, 2, 2, 5, 1, 3, 5, 3, 9, 10, 10, 5, 6, 9, 5, 3, 3, 2, 10, 7, 6, 7, 7, 5, 7, 7, 8, 5, 6, 8, 10, 5, 5, 4, 8, 9, 5, 3, 8, 3, 11, 9, 7, 6, 7, 5, 8, 6, 3, 5, 2, 5, 6, 4, 10, 8, 11, 8, 7, 7, 9, 3, 8, 5, 5, 11, 8, 5, 4, 8, 1, 5, 6, 4, 9, 8, 4, 4, 7, 6, 4, 11, 7, 11, 4, 7, 10, 2, 3, 7, 3, 7, 3, 11, 6, 5, 6, 8, 6, 11, 7, 2, 5, 4, 10, 1, 10, 9, 5, 9, 8, 8, 11, 7, 8, 5, 6, 5, 6, 6, 10, 8, 10, 6, 2, 1, 8, 1, 6, 11, 1, 6, 5, 5, 7, 2, 1, 4, 7, 8, 4, 5, 3, 4, 10, 9, 6, 6, 4, 7, 9, 10, 8, 8, 10, 10, 3, 7, 4, 4, 4, 8, 3, 3, 3, 9, 6, 7, 10, 5, 6, 6, 8, 8, 3, 6, 7, 5, 4, 11, 5, 5, 6, 9, 3, 5, 11, 8, 6, 8, 7, 9, 4, 9, 9, 8, 6, 8, 8, 2, 3, 9, 8, 6, 1, 10, 5, 4, 10, 8, 1, 8, 9, 7, 4, 3, 1, 9, 5, 2, 11, 6, 7, 2, 11, 4, 8, 10, 1, 10, 11, 5, 10, 7, 7, 5, 2, 6, 9, 9, 6, 5, 8, 6, 10, 10, 9, 7, 10, 5, 9, 7, 6, 7, 6, 4, 3, 6, 5, 8, 7, 5, 4, 5, 9, 7, 8, 5, 4, 2, 5, 1, 8, 4, 5, 11, 3, 6, 8, 2, 6, 9, 2, 4, 8, 10, 7, 9, 6, 9, 2, 3, 3, 9, 7, 9, 4, 7, 11, 8, 5, 3, 7, 5, 7, 5, 8, 10, 9, 10, 3, 7, 6, 7, 8, 5, 9, 3, 1, 7, 9, 10, 9, 6, 5, 9, 3, 9, 7, 4, 9, 9, 7, 6, 4, 9, 9, 7, 5, 8, 11, 1, 2, 5, 6, 9, 10, 10, 10, 8, 8, 5, 7, 3, 3, 8, 1, 3, 4, 8, 10, 8, 10, 7, 7, 4, 5, 4, 10, 10, 11, 4, 7, 8, 10, 7, 4, 2, 5, 7, 8, 5, 6, 9, 5, 4, 5, 6, 7, 11, 9, 5, 5, 6, 9, 4, 7, 8, 9, 5, 8, 8, 11, 10, 9, 6, 9, 4, 8, 1, 9, 9, 6, 8, 11, 8, 10, 1, 11, 10, 4, 3, 10, 5, 6, 10, 9, 4, 1, 7, 2, 3, 6, 10, 2, 6, 4, 6, 7, 11, 7, 9, 10, 10, 7, 7, 1, 7, 12, 5, 7, 9, 1, 11, 1, 8, 6, 10, 6, 4, 6, 3, 4, 7, 5, 5, 5, 9, 7, 5, 3, 6, 9, 9, 4, 11, 8, 3, 6, 10, 7, 7, 3, 3, 8, 10, 6, 4, 1, 7, 7, 8, 9, 2, 3, 11, 4, 7, 4, 9, 9, 8, 7, 1, 4, 8, 6, 10, 8, 8, 9, 5, 10, 6, 8, 4, 2, 6, 10, 8, 6, 4, 8, 5, 8, 3, 8, 10, 4, 10, 10, 5, 4, 7, 12, 5, 11, 10]\n",
      "水平误差： [0, 0, 5.760598467664905, 0, 0, 7.358661982156054, 5.505657630894738, 5.991083438227855, 0, 0, 14.07480695608065, 0, 10.151063397269397, 0, 0, 0, 0, 10.286039018351548, 0, 9.336256332829313, 5.9937161824121485, 0, 0, 0, 0, 8.972567263701414, 7.927033919358363, 0, 0, 0, 0, 0, 10.112584866173336, 5.171027179187759, 10.495633867030271, 8.813761157564974, 0, 0, 10.12272753537951, 0, 0, 0, 14.681545811153995, 9.758432927425952, 0, 11.458031894549494, 7.476561195229284, 4.9624507681513315, 14.095408169838189, 6.315686787101292, 0, 12.161933443844632, 0, 4.651640222519086, 0, 7.761002301587028, 6.816184160225267, 0, 6.500849190370781, 11.713134745773864, 0, 8.15329698576569, 0, 0, 9.58676146801276, 0, 11.594741533156727, 6.7578530074965615, 0, 0, 0, 0, 0, 0, 0, 0, 9.36439615608439, 9.067634360795148, 10.192165573243958, 0, 0, 12.67982581725367, 0, 6.853567498534185, 0, 14.194668078173654, 0, 2.6506502075459353, 3.1045446954559894, 12.366920444278195, 9.001101549231322, 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      "垂直误差： [0, 8.65067702856891, 0, 14.705260117011157, 13.31872236580908, 0, 0, 0, 16.529863717466146, 6.282531825289309, 0, 13.383859803794616, 0, 6.7868022772028676, 14.968186987772585, 15.29011735239017, 4.03639073887737, 0, 13.85718734086825, 0, 0, 15.799438375351459, 11.467190201368755, 16.33908858873787, 11.11342994594547, 0, 0, 6.5138682829477315, 5.995366388015233, 17.821378276879177, 6.414570969116314, 9.940019423560905, 0, 0, 0, 0, 12.244402314655344, 14.252099928877415, 0, 17.07832343003088, 9.35010520699501, 9.786680223441419, 0, 0, 5.173844342405415, 0, 0, 0, 0, 0, 8.860509253959304, 0, 17.197013621046874, 0, 6.526405513681848, 0, 0, 8.940435947671483, 0, 0, 8.721636680201117, 0, 15.955978956225117, 12.949396488078516, 0, 11.449665440680242, 0, 0, 8.908448633968758, 8.80734226702942, 8.886706471362494, 4.9073727182857265, 14.791989981323498, 15.196651205950182, 15.92598108142327, 9.441536958924914, 0, 0, 0, 13.781472993660305, 14.83805118871343, 0, 9.31177023053567, 0, 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4.2945827435762, 0, 0, 15.569418206855872, 9.810847980008802, 0, 15.685131606720189, 0, 10.550139420537281, 5.728007184031235, 9.62643703834855, 13.573751667950178, 0, 0, 6.625429263938027, 10.034988924564473, 13.104177064429685, 14.558735638678007, 19.305560464340264, 0, 19.859069861224878, 0, 0, 5.319609366923471, 0, 8.76774979907625, 0, 0, 4.149481498467423, 4.722046184269243, 7.078001581016133, 0, 0, 13.075372738157418, 2.4053386167550115, 12.010738484984387, 0, 13.696214525594607, 3.437301639791084, 6.0697694070461266, 10.591781525694763, 8.365759110093617, 0, 9.545440413628095, 13.843274937846934, 15.678554805122136, 14.7532762793649, 11.579571769788961, 0, 0, 10.96138684042625, 18.052584678456398, 16.701257710951108, 0, 0, 0, 0, 0, 7.252210630170113, 16.08138270033499, 0, 11.507489988361248, 0, 12.986890276540349, 8.353018095525487, 0, 4.528838131329445, 4.949258250494447, 0, 16.97269128962225]\n"
     ]
    }
   ],
   "source": [
    "def dijkstra(start, point_type, graph, alpha1, alpha2, beta1, beta2, theta, delta):\n",
    "    '''\n",
    "    :param start: 起点下标\n",
    "    :param point_type: 点的类型：水平校正点为0，垂直校正点为1，起点为2，终点为3\n",
    "    :param graph: 距离矩阵\n",
    "    :return:\n",
    "            dist: 到达每个节点的最短路径列表\n",
    "            pre_node_list: 前驱节点列表\n",
    "            node_num_list: 到达每个点需要经过的节点个数\n",
    "            h_error:水平误差\n",
    "            v_error:垂直误差\n",
    "    '''\n",
    "    if graph is None:\n",
    "        return None\n",
    "    #到达每个节点的最短距离向量\n",
    "    dist = [float(\"inf\")] * len(graph)\n",
    "    dist[start] = 0\n",
    "    # 水平误差\n",
    "    h_error = [float(\"inf\")] * len(graph)\n",
    "    h_error[start] = 0\n",
    "    # 垂直误差\n",
    "    v_error = [float(\"inf\")] * len(graph)\n",
    "    v_error[start] = 0\n",
    "    #访问过的节点列表\n",
    "    S = []\n",
    "    #未访问过的节点列表\n",
    "    Q = [x for x in range(len(graph))]\n",
    "    #初始节点与其他节点的距离\n",
    "    dis_list = [i for i in graph[start]]\n",
    "    #前驱节点列表\n",
    "    pre_node_list = [float(\"inf\")] * len(graph)\n",
    "    #到达每个节点需要经过的节点数量\n",
    "    node_num_list = [float(\"inf\")] * len(graph)\n",
    "    node_num_list[start] = 0\n",
    "    while Q:\n",
    "        #找出初始节点与其他节点的最近距离且该节点必须在未访问的节点列表中\n",
    "        u_dist = min([d for v, d in enumerate(dis_list) if v in Q])\n",
    "        if u_dist == float(\"inf\"):\n",
    "            break\n",
    "        #求出该点下标\n",
    "        u = dis_list.index(u_dist)\n",
    "        #将u节点添加到访问过的节点列表\n",
    "        S.append(u)\n",
    "        #将u节点移出未访问过的节点列表\n",
    "        Q.remove(u)\n",
    "        #寻找u的下一个节点v\n",
    "        for v,d in enumerate(graph[u]):\n",
    "            if d != float(\"inf\"):\n",
    "                #如果起点到v的距离大于起点到u的距离才需要更新，否则保持\n",
    "                if dist[v] > dist[u] + d:\n",
    "                    #如果v点为水平校正点\n",
    "                    if point_type[v] == 0 and h_error[u] + d * delta < beta2 and v_error[u] + d * delta < beta1:\n",
    "                        #清空水平误差\n",
    "                        h_error[v] = 0\n",
    "                        #对到v点的距离进行赋值\n",
    "                        dist[v] = dist[u] + d\n",
    "                        dis_list[v] = dist[v]\n",
    "                        #更新垂直误差\n",
    "                        v_error[v] = v_error[u] + d*delta\n",
    "                        #更新前驱节点列表\n",
    "                        pre_node_list[v] = u\n",
    "                        #更新到达每个节点需要经过的节点数量\n",
    "                        node_num_list[v] = node_num_list[u] + 1\n",
    "                    # 如果v点为垂直校正点\n",
    "                    if point_type[v] == 1 and h_error[u] + d * delta < alpha2 and v_error[u] + d * delta < alpha1:\n",
    "                        #清空垂直误差\n",
    "                        v_error[v] = 0\n",
    "                        #对到v点的距离进行赋值v\n",
    "                        dist[v] = dist[u] + d\n",
    "                        dis_list[v] = dist[v]\n",
    "                        #更新垂直误差\n",
    "                        h_error[v] = h_error[u] + d*delta\n",
    "                        #更新前驱节点列表\n",
    "                        pre_node_list[v] = u\n",
    "                        #更新到达每个节点需要经过的节点数量\n",
    "                        node_num_list[v] = node_num_list[u] + 1\n",
    "                    # 如果v点为终点\n",
    "                    if point_type[v] == 3 and h_error[u] + d * delta < theta and v_error[u] + d * delta < theta:\n",
    "                        #更新水平误差\n",
    "                        h_error[v] = h_error[u] + d*delta\n",
    "                        #更新垂直误差\n",
    "                        v_error[v] = v_error[u] + d*delta\n",
    "                        #对到v点的距离进行赋值v\n",
    "                        dist[v] = dist[u] + d\n",
    "                        #更新前驱节点列表\n",
    "                        pre_node_list[v] = u\n",
    "                        #更新到达每个节点需要经过的节点数量\n",
    "                        node_num_list[v] = node_num_list[u] + 1\n",
    "\n",
    "    return dist, pre_node_list, node_num_list,h_error,v_error\n",
    "dist, pre_node_list, node_num_list,h_error,v_error = dijkstra(start=0,point_type=data[:,3],graph=dis_mat,alpha1=25,alpha2=15,beta1=20,beta2=25,theta=30,delta=0.001)\n",
    "print(\"到达每个节点的最短路径列表:\",dist)\n",
    "print(\"=\"*120)\n",
    "print(\"前驱节点列表：\",pre_node_list)\n",
    "print(\"=\"*120)\n",
    "print(\"到达每个点需要经过的节点个数:\",node_num_list)\n",
    "print(\"水平误差：\",h_error)\n",
    "print(\"垂直误差：\",v_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5a8146",
   "metadata": {},
   "source": [
    "## 7.生成最优路径和最短距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44cb4b3f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优路径： [0, 503, 294, 91, 607, 170, 278, 369, 214, 397, 612]\n",
      "到达终点的最短距离： 104065.88224919878\n"
     ]
    }
   ],
   "source": [
    "def generate_path(pre_node_list,last):\n",
    "    path = [last]\n",
    "    temp = last\n",
    "    while temp != 0:\n",
    "        index = pre_node_list[temp]\n",
    "        path.append(index)\n",
    "        temp = index\n",
    "    return list(reversed(path))\n",
    "path = generate_path(pre_node_list,last=len(data)-1)\n",
    "print(\"最优路径：\",path)\n",
    "print(\"到达终点的最短距离：\",dist[n])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb83cb1",
   "metadata": {},
   "source": [
    "## 8.到达矫正点之前的误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aeec08e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "矫正前的水平误差： [0, 13.387919852713356, 23.568730575283162, 7.354701385785341, 15.707719481310829, 3.5929194950164933, 14.049952710061055, 11.436133608421184, 24.74970531390239, 9.0170828790191, 25.98977416864135]\n",
      "矫正前的垂直误差： [0, 13.387919852713356, 10.180810722569804, 17.535512108355146, 8.353018095525487, 11.94593759054198, 10.45703321504456, 21.893166823465744, 13.313571705481205, 22.330654584500305, 16.97269128962225]\n"
     ]
    }
   ],
   "source": [
    "def generate_error(path,h_error,v_error):\n",
    "    h_err = [0]\n",
    "    v_err = [0]\n",
    "    for index in range(1,len(path)):\n",
    "        if h_error[path[index]]==0:\n",
    "            h_err.append(h_error[path[index-1]]+v_error[path[index]])\n",
    "        else:\n",
    "            h_err.append(h_error[path[index]])\n",
    "        if v_error[path[index]]==0:\n",
    "            v_err.append(v_error[path[index-1]]+h_error[path[index]])\n",
    "        else:\n",
    "            v_err.append(v_error[path[index]])\n",
    "    return h_err,v_err\n",
    "h_err,v_err = generate_error(path,h_error,v_error)\n",
    "print(\"矫正前的水平误差：\",h_err)\n",
    "print(\"矫正前的垂直误差：\",v_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7295916",
   "metadata": {},
   "source": [
    "## 9.绘制最短路径图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30b1dda3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_8220/741021957.py:31: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.\n",
      "  ax = Axes3D(fig)\n",
      "C:\\Users\\ADMINI~1\\AppData\\Local\\Temp/ipykernel_8220/741021957.py:8: MatplotlibDeprecationWarning: \n",
      "The M attribute was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use self.axes.M instead.\n",
      "  xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Arrow3D(FancyArrowPatch):\n",
    "    def __init__(self, xs, ys, zs, *args, **kwargs):\n",
    "        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\n",
    "        self._verts3d = xs, ys, zs\n",
    "\n",
    "    def draw(self, renderer):\n",
    "        xs3d, ys3d, zs3d = self._verts3d\n",
    "        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n",
    "        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n",
    "        FancyArrowPatch.draw(self, renderer)\n",
    "def plot_path(data,path):\n",
    "    #### 起点 ####\n",
    "    Xa = data[0][0]\n",
    "    Ya = data[0][1]\n",
    "    Za = data[0][2]\n",
    "    #### 终点 ####\n",
    "    Xb = data[n][0]\n",
    "    Yb = data[n][1]\n",
    "    Zb = data[n][2]\n",
    "    #### 水平校正点 ####\n",
    "    Xh = data[data[:,3]==0][:,0]\n",
    "    Yh = data[data[:,3]==0][:,1]\n",
    "    Zh = data[data[:,3]==0][:,2]\n",
    "    #### 垂直校正点 ####\n",
    "    Xv = data[data[:,3]==1][:,0]\n",
    "    Yv = data[data[:,3]==1][:,1]\n",
    "    Zv = data[data[:,3]==1][:,2]\n",
    "\n",
    "    #### 绘制三维散点图 ####\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "    ax.scatter(Xa,Ya,Za, c='r', s=10, label='起点')\n",
    "    ax.scatter(Xb,Yb,Zb, c='g', s=10, label='终点')\n",
    "    ax.scatter(Xh,Yh,Zh, c='b', s=10, label='水平校正点')\n",
    "    ax.scatter(Xv,Yv,Zv, c='y', s=10, label='垂直校正点')\n",
    "\n",
    "    # 绘制图例\n",
    "    ax.legend(loc='best')\n",
    "\n",
    "    # 添加坐标轴(顺序是Z, Y, X)\n",
    "    ax.set_zlabel('Z', fontdict={'size': 15, 'color': 'red'})\n",
    "    ax.set_ylabel('Y', fontdict={'size': 15, 'color': 'red'})\n",
    "    ax.set_xlabel('X', fontdict={'size': 15, 'color': 'red'})\n",
    "\n",
    "    ax.view_init(elev=10., azim=-80)\n",
    "    # 展示\n",
    "    start = 0\n",
    "    while start<len(path)-1:\n",
    "        pre = path[start]\n",
    "        next = path[start + 1]\n",
    "        # ax.plot([data[pre][0], data[next][0]], [data[pre][1], data[next][1]],\n",
    "        #             [data[pre][2], data[next][2]], color='red', alpha=0.8, lw=3)\n",
    "        a = Arrow3D([data[pre][0], data[next][0]], [data[pre][1], data[next][1]],\n",
    "                    [data[pre][2], data[next][2]], mutation_scale=10,\n",
    "                    lw=1, arrowstyle=\"-|>\", color=\"r\")\n",
    "        ax.add_artist(a)\n",
    "        start = start+1\n",
    "    plt.title(\"最短路径图\")\n",
    "    plt.draw()\n",
    "    plt.show()\n",
    "plot_path(data,path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": [
     "## 导入包\n"
    ]
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}