{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## hw2 - searching the burning building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hw2a import *\n",
    "from notebook4e import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hours\n",
      "# is it greater than 0?\n",
      " OK  got: 2 expected: <function main.<locals>.<lambda> at 0x7ea5e3552cb0>\n",
      "temp(node)\n",
      " OK  got: 70 expected: 70\n",
      " OK  got: 500 expected: 500\n",
      " OK  got: 60 expected: 60\n",
      "No temp for node:  ENTRANCE\n",
      " OK  got: None expected: None\n",
      "room_depth_first\n",
      " OK  got: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20'], 125, 26) expected: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20'], 125, 26)\n",
      " OK  got: (['S30', 'S40', 'H40', 'H41', 'H42', 'H43', 'H44', 'H45', 'H46', 'H47', 'H48', 'H49', 'S49', 'S39', 'S29', 'S19', 'EXIT'], 115, 21) expected: (['S30', 'S40', 'H40', 'H41', 'H42', 'H43', 'H44', 'H45', 'H46', 'H47', 'H48', 'H49', 'S49', 'S39', 'S29', 'S19', 'EXIT'], 115, 21)\n",
      " OK  got: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 150, 68) expected: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 150, 68)\n",
      " OK  got: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20'], 125, 26) expected: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20'], 125, 26)\n",
      "room_breadth_first\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 77) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 77)\n",
      " OK  got: (['H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'H26', 'H27', 'H28', 'H29', 'S29', 'S19', 'EXIT'], 75, 83) expected: (['H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'H26', 'H27', 'H28', 'H29', 'S29', 'S19', 'EXIT'], 75, 83)\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'H35', 'R35'], 60, 51) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'H35', 'R35'], 60, 51)\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 77) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 77)\n",
      "room_best_first\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 23) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 23)\n",
      " OK  got: (['S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'H16', 'H17', 'H18', 'H19', 'S19', 'EXIT'], 75, 23) expected: (['S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'H16', 'H17', 'H18', 'H19', 'S19', 'EXIT'], 75, 23)\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 120, 85) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 120, 85)\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 23) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 23)\n",
      "room_worst_first\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H33', 'H32', 'H31', 'H30', 'S30', 'S20'], 105, 36) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H33', 'H32', 'H31', 'H30', 'S30', 'S20'], 105, 36)\n",
      " OK  got: (['S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'R34', 'R35', 'R36', 'H36', 'H37', 'H38', 'H39', 'S39', 'S29', 'S19', 'EXIT'], 105, 85) expected: (['S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'R34', 'R35', 'R36', 'H36', 'H37', 'H38', 'H39', 'S39', 'S29', 'S19', 'EXIT'], 105, 85)\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35'], 60, 10) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35'], 60, 10)\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H33', 'H32', 'H31', 'H30', 'S30', 'S20'], 105, 36) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H33', 'H32', 'H31', 'H30', 'S30', 'S20'], 105, 36)\n",
      "room_astar\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 18) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20'], 75, 18)\n",
      " OK  got: (['H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'H26', 'H27', 'H28', 'H29', 'S29', 'S19', 'EXIT'], 75, 14) expected: (['H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'H26', 'H27', 'H28', 'H29', 'S29', 'S19', 'EXIT'], 75, 14)\n",
      "\n",
      "debug examples\n",
      "\n",
      " OK  got: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15'], 170, 34, ['EXIT', 'S19', 'S29', 'S39', 'S49', 'H49', 'R49', 'H48', 'R48', 'H47', 'R47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'R43', 'R42', 'H42', 'H41', 'R41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15']) expected: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15'], 170, 34, ['EXIT', 'S19', 'S29', 'S39', 'S49', 'H49', 'R49', 'H48', 'R48', 'H47', 'R47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'R43', 'R42', 'H42', 'H41', 'R41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15'])\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 31, ['EXIT', 'S19', 'H19', 'S29', 'H18', 'R19', 'H29', 'S39', 'H17', 'R18', 'H28', 'R29', 'H39', 'S49', 'H16', 'R17', 'H27', 'R28', 'H38', 'R39', 'H49', 'H15', 'R16', 'H26', 'R27', 'H37', 'R38', 'H48', 'R49', 'H14', 'R15']) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 31, ['EXIT', 'S19', 'H19', 'S29', 'H18', 'R19', 'H29', 'S39', 'H17', 'R18', 'H28', 'R29', 'H39', 'S49', 'H16', 'R17', 'H27', 'R28', 'H38', 'R39', 'H49', 'H15', 'R16', 'H26', 'R27', 'H37', 'R38', 'H48', 'R49', 'H14', 'R15'])\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 17, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15']) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 17, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15'])\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 67, ['EXIT', 'S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H35', 'H33', 'H32', 'H31', 'H30', 'R31', 'R32', 'R33', 'R37', 'R38', 'R39', 'S30', 'H29', 'H28', 'H27', 'H26', 'H25', 'R25', 'H24', 'H23', 'H22', 'H21', 'H20', 'S20', 'S40', 'H40', 'H41', 'H42', 'H43', 'H44', 'R44', 'R45', 'R46', 'H45', 'H46', 'H47', 'H48', 'H49', 'R49', 'S49', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15']) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 67, ['EXIT', 'S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35', 'R34', 'H34', 'H35', 'H33', 'H32', 'H31', 'H30', 'R31', 'R32', 'R33', 'R37', 'R38', 'R39', 'S30', 'H29', 'H28', 'H27', 'H26', 'H25', 'R25', 'H24', 'H23', 'H22', 'H21', 'H20', 'S20', 'S40', 'H40', 'H41', 'H42', 'H43', 'H44', 'R44', 'R45', 'R46', 'H45', 'H46', 'H47', 'H48', 'H49', 'R49', 'S49', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15'])\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 8, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15']) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'], 40, 8, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'R15'])\n",
      " OK  got: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 150, 68, ['EXIT', 'S19', 'S29', 'S39', 'S49', 'H49', 'R49', 'H48', 'R48', 'H47', 'R47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'R43', 'R42', 'H42', 'H41', 'R41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15', 'R16', 'H16', 'H17', 'H18', 'R18', 'R17', 'R14', 'R13', 'R12', 'R11', 'H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'R25', 'R26', 'H26', 'H27', 'H28', 'R28', 'R27', 'R24', 'R23', 'R22', 'R21', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35']) expected: (['S19', 'S29', 'S39', 'S49', 'H49', 'H48', 'H47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'H42', 'H41', 'H40', 'S40', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 150, 68, ['EXIT', 'S19', 'S29', 'S39', 'S49', 'H49', 'R49', 'H48', 'R48', 'H47', 'R47', 'H46', 'R46', 'R45', 'R44', 'H44', 'H43', 'R43', 'R42', 'H42', 'H41', 'R41', 'H40', 'S40', 'S30', 'S20', 'S10', 'H10', 'H11', 'H12', 'H13', 'H14', 'H15', 'R15', 'R16', 'H16', 'H17', 'H18', 'R18', 'R17', 'R14', 'R13', 'R12', 'R11', 'H20', 'H21', 'H22', 'H23', 'H24', 'H25', 'R25', 'R26', 'H26', 'H27', 'H28', 'R28', 'R27', 'R24', 'R23', 'R22', 'R21', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'])\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'H35', 'R35'], 60, 51, ['EXIT', 'S19', 'H19', 'S29', 'H18', 'R19', 'H29', 'S39', 'H17', 'R18', 'H28', 'R29', 'H39', 'S49', 'H16', 'R17', 'H27', 'R28', 'H38', 'R39', 'H49', 'H15', 'R16', 'H26', 'R27', 'H37', 'R38', 'H48', 'R49', 'H14', 'R15', 'H25', 'R26', 'H36', 'R37', 'H47', 'R48', 'H13', 'R14', 'H24', 'R25', 'H35', 'R36', 'H46', 'R47', 'H12', 'R13', 'H23', 'R24', 'H34', 'R35']) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'H35', 'R35'], 60, 51, ['EXIT', 'S19', 'H19', 'S29', 'H18', 'R19', 'H29', 'S39', 'H17', 'R18', 'H28', 'R29', 'H39', 'S49', 'H16', 'R17', 'H27', 'R28', 'H38', 'R39', 'H49', 'H15', 'R16', 'H26', 'R27', 'H37', 'R38', 'H48', 'R49', 'H14', 'R15', 'H25', 'R26', 'H36', 'R37', 'H47', 'R48', 'H13', 'R14', 'H24', 'R25', 'H35', 'R36', 'H46', 'R47', 'H12', 'R13', 'H23', 'R24', 'H34', 'R35'])\n",
      " OK  got: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 120, 85, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'S10', 'S20', 'H20', 'H21', 'R21', 'H22', 'R22', 'R23', 'H23', 'H24', 'R24', 'H25', 'H26', 'R26', 'H27', 'R27', 'H28', 'R28', 'H29', 'R29', 'S29', 'R25', 'S30', 'S40', 'H40', 'H41', 'R41', 'H42', 'R42', 'R43', 'H43', 'H44', 'H45', 'H46', 'H47', 'R47', 'H48', 'R48', 'H49', 'R49', 'S49', 'H30', 'H31', 'H32', 'H33', 'R31', 'R32', 'R33', 'R44', 'R46', 'S39', 'H39', 'H38', 'H37', 'R37', 'R38', 'R39', 'H34', 'H35', 'H36', 'R45', 'R34', 'R36', 'R35']) expected: (['S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'S10', 'S20', 'S30', 'H30', 'H31', 'H32', 'H33', 'H34', 'H35', 'R35'], 120, 85, ['EXIT', 'S19', 'H19', 'H18', 'H17', 'H16', 'H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'R11', 'R12', 'R13', 'R14', 'R15', 'R16', 'R17', 'R18', 'R19', 'S10', 'S20', 'H20', 'H21', 'R21', 'H22', 'R22', 'R23', 'H23', 'H24', 'R24', 'H25', 'H26', 'R26', 'H27', 'R27', 'H28', 'R28', 'H29', 'R29', 'S29', 'R25', 'S30', 'S40', 'H40', 'H41', 'R41', 'H42', 'R42', 'R43', 'H43', 'H44', 'H45', 'H46', 'H47', 'R47', 'H48', 'R48', 'H49', 'R49', 'S49', 'H30', 'H31', 'H32', 'H33', 'R31', 'R32', 'R33', 'R44', 'R46', 'S39', 'H39', 'H38', 'H37', 'R37', 'R38', 'R39', 'H34', 'H35', 'H36', 'R45', 'R34', 'R36', 'R35'])\n",
      " OK  got: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35'], 60, 10, ['EXIT', 'S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35']) expected: (['S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35'], 60, 10, ['EXIT', 'S19', 'S29', 'S39', 'H39', 'H38', 'H37', 'H36', 'R36', 'R35'])\n"
     ]
    }
   ],
   "source": [
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.int = int        # Turnaround to resolve deprecated conflict\n",
    "# import networkx as nx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import lines\n",
    "\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data for burning building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "xnode_colors = {node: 'white' for node in room_map.locations.keys()}\n",
    "xnode_positions = room_map.locations\n",
    "xnode_label_pos = { k:[v[0],v[1]-10]  for k,v in room_map.locations.items() }\n",
    "xedge_weights = {(k, k2) : v2 for k, v in room_map.graph_dict.items() for k2, v2 in v.items()}\n",
    "\n",
    "room_graph_data = {  'graph_dict' : room_map.graph_dict,\n",
    "                     'node_colors': xnode_colors,\n",
    "                     'node_positions': xnode_positions,\n",
    "                     'node_label_positions': xnode_label_pos,\n",
    "                     'edge_weights': xedge_weights\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'H10': (40, 50), 'H11': (50, 50), 'H12': (60, 50), 'H13': (70, 50), 'H14': (80, 50), 'H15': (90, 50), 'H16': (100, 50), 'H17': (110, 50), 'H18': (120, 50), 'H19': (130, 50), 'S10': (40, 50), 'R11': (50, 60), 'R12': (60, 60), 'R13': (70, 60), 'R14': (80, 60), 'R15': (90, 60), 'R16': (100, 60), 'R17': (110, 60), 'R18': (120, 60), 'R19': (130, 60), 'S19': (140, 50), 'EXIT': (165, 40), 'H20': (40, 75), 'H21': (50, 75), 'H22': (60, 75), 'H23': (70, 75), 'H24': (80, 75), 'H25': (90, 75), 'H26': (100, 75), 'H27': (110, 75), 'H28': (120, 75), 'H29': (130, 75), 'S20': (40, 75), 'R21': (50, 85), 'R22': (60, 85), 'R23': (70, 85), 'R24': (80, 85), 'R25': (90, 85), 'R26': (100, 85), 'R27': (110, 85), 'R28': (120, 85), 'R29': (130, 85), 'S29': (140, 75), 'H30': (40, 100), 'H31': (50, 100), 'H32': (60, 100), 'H33': (70, 100), 'H34': (80, 100), 'H35': (90, 100), 'H36': (100, 100), 'H37': (110, 100), 'H38': (120, 100), 'H39': (130, 100), 'S30': (40, 100), 'R31': (50, 110), 'R32': (60, 110), 'R33': (70, 110), 'R34': (80, 110), 'R35': (90, 110), 'R36': (100, 110), 'R37': (110, 110), 'R38': (120, 110), 'R39': (130, 110), 'S39': (140, 100), 'H40': (40, 125), 'H41': (50, 125), 'H42': (60, 125), 'H43': (70, 125), 'H44': (80, 125), 'H45': (90, 125), 'H46': (100, 125), 'H47': (110, 125), 'H48': (120, 125), 'H49': (130, 125), 'S40': (40, 125), 'R41': (50, 135), 'R42': (60, 135), 'R43': (70, 135), 'R44': (80, 135), 'R45': (90, 135), 'R46': (100, 135), 'R47': (110, 135), 'R48': (120, 135), 'R49': (130, 135), 'S49': (140, 125)}\n"
     ]
    }
   ],
   "source": [
    "room_locations = room_map.locations\n",
    "print(room_locations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_1326870/4033054162.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroom_graph_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/httpd/html/zoo/classes/cs370/aima/notebook4e.py\u001b[0m in \u001b[0;36mshow_map\u001b[0;34m(graph_data, node_colors)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    965\u001b[0m     \u001b[0;31m# add edge lables to the graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 966\u001b[0;31m     \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_networkx_edge_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_positions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medge_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m     \u001b[0;31m# add a legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw_networkx_edge_labels\u001b[0;34m(G, pos, edge_labels, label_pos, font_size, font_color, font_family, font_weight, alpha, bbox, horizontalalignment, verticalalignment, ax, rotate, clip_on, node_size, nodelist, connectionstyle, hide_ticks)\u001b[0m\n\u001b[1;32m   1555\u001b[0m             )\n\u001b[1;32m   1556\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1557\u001b[0;31m             text_items[edge] = CurvedArrowText(\n\u001b[0m\u001b[1;32m   1558\u001b[0m                 \u001b[0marrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1559\u001b[0m                 \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arrow, label_pos, labels_horizontal, ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1370\u001b[0m                 \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1371\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1372\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mangle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_text_pos_angle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;31m# Create text object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36m_update_text_pos_angle\u001b[0;34m(self, arrow)\u001b[0m\n\u001b[1;32m   1408\u001b[0m             \u001b[0;31m# Fractional label position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m             \u001b[0mpath_disp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_arrow_path_disp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1410\u001b[0;31m             \u001b[0;34m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_disp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1411\u001b[0m             \u001b[0;31m# Text position at a proportion t along the line in display coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1412\u001b[0m             \u001b[0;31m# default is 0.5 so text appears at the halfway point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
     ]
    },
    {
     "data": {
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ZMq6OBhQKFI8AAAAAAKBAy8rK0oYNG2Sz2bRs2TJ16NBBVqtVzz77rHx8fFwdDyiwKB4BAAAAAEChcePGDS1YsEA2m00nTpzQ0KFDZbVaVadOHVdHAwocikcAAAAAAFAoHT58WDabTbNmzVLlypVltVo1cOBAlShRwtXRgAKB4hEAAAAAABRqGRkZWrNmjSIjI7Vu3Tp1795dVqtV7du3l5eXl6vjAR6L4hEAAAAAAOD/JCYmas6cOYqMjNS1a9cUHh6u8PBwVa1a1dXRAI9D8QgAAAAAAHAPe/bskc1m09y5c9WgQQNZrVb17t1bxYoVc3U0wCNQPAIAAAAAADxAWlqavv32W9lsNm3dulV9+/ZVRESEmjdvLovF4up4gNuieAQAAAAAAMilX375RbNmzZLNZpO3t7esVquGDRumkJAQV0cD3A7FIwAAAAAAQB4ZhqHNmzcrMjJSS5YsUevWrWW1WtW9e3cVKVLE1fEAt0DxCAAAAAAAYIfk5GQtXrxYNptNhw4d0pAhQ2S1WtWgQQNXRwNciuIRAAAAAADAJMePH9eMGTM0c+ZMlS1bVlarVYMGDVKpUqVcHQ1wOopHAAAAAAAAk2VmZiomJkY2m02rVq3S008/rYiICHXq1Ene3t6ujgc4BcUjAAAAAACAA129elVz586VzWZTQkKChg8fLqvVqho1arg6GuBQFI8AAAAAAABO8uOPP8pms2n27Nl64oknZLVa1a9fPwUGBro6GmA6ikcAAAAAAAAnu337tlauXKnIyEht3LhRzz//vKxWq1q3bi2LxeLqeIApKB4BAAAAAABcKCEhQf/5z38UGRmp9PR0Wa1WDR8+XBUrVnR1NMAuFI8AAAAAAABuwDAMbd++XTabTQsWLNBTTz2liIgI9ezZU35+fq6OB+QZxSMAAAAAAICbSUlJ0TfffCObzaa9e/dq4MCBioiIUKNGjfgqNjwGxSMAAAAAAIAbO3XqlGbOnKkZM2aoePHislqtGjJkiMqUKePqaMADUTwCAAAAAAB4gKysLH3//feKjIzU8uXL1bFjR1mtVj3zzDPy8fFxdTzgLhSPAAAAAAAAHub69etasGCBIiMjdfr0aQ0bNkxWq1W1atVydTQgG8UjAAAAAACAB/vpp580Y8YMzZo1S1WrVlVERIQGDBig4sWLuzoaCjmKRwAAAAAAgAIgIyND0dHRstlsiomJUY8ePRQREaF27drJy8vL1fFQCFE8AgAAAAAAFDCXLl3S7NmzZbPZdOPGDYWHhys8PFxVqlRxdTQUIhSPAAAAAAAABZRhGNqzZ48iIyM1b948NWzYUFarVb1795a/v7+r46GAo3gEAAAAAAAoBFJTU/Xtt98qMjJS27dvV//+/WW1WvXUU0/JYrG4Oh4KIIpHAAAAAACAQubcuXOaNWuWbDabihQpIqvVqqFDh6p8+fKujoYChOIRAAAAAACgkDIMQ5s2bZLNZtM333yjNm3aKCIiQt26dZOvr6+r48HDUTwCAAAAAABAycnJWrhwoWw2mw4fPqwhQ4YoIiJC9erVc3U0eCiKRwAAAAAAAORw9OhRzZgxQzNnzlRISIisVqsGDRqkRx55xJT7T09P14kTJ3T16lX5+fmpQoUKKlOmjCn3DfdB8QgAAAAAAIB7yszM1Lp16xQZGanVq1fr2WefldVqVceOHeXt7Z3n+zt37py2bt0qX19f1apVS4888ohSU1N15swZJSQkKDg4WO3atZOXl5cDtgbORvEIAAAAAACAh7py5Yrmzp0rm82mixcvasSIEQoPD1f16tUfum5WVpYWLVqkSpUqKTQ09L5X0U5MTNSaNWv01FNPqUaNGmZvApyM4hEAAAAAAAB5sn//ftlsNs2ePVu1a9dWRESE+vbtq4CAgLuWNQxDUVFR6tWrl0qUKJGr+1+zZo0qV66sWrVqmR0dTsRxqx7m0qVLGjVqlKpWraqiRYuqXLly6tixo9auXStJWrJkiZ5++mmVKVNGFotFcXFxd91HWlqa/vSnP6l06dIKCAhQz549de7cOSdvifsIDw9X9+7d7/r5zp07ZbFYdOrUKUnSq6++qqZNm8rPz09Vq1a9a/nU1FSFh4erQYMG8vX1VVhYmGODuzGzZhoXF6devXopJCRExYoVU4MGDRQZGeng9O7LrLkeOnRI7du3V7ly5eTn56dq1appwoQJun37toO3wD2ZNdffOnr0qIKCghQYGOiAxO7PrJmeOnVKFovlrlt0dLSDt8A9mbmvGoahzz77TLVq1VLRokUVEhKit956y4Hp3ZdZc/3ggw/uub9aLBZdvHjRwVvhXszcV1evXq0WLVooKChIpUuXVq9evXTkyBEHpndfZs51wYIFatiwoYoVK6YqVarok08+cWBy92fGe6zjx4/r+eefV5kyZVS8eHH1799fFy5ccPKWAM7VoEEDTZkyRefOndOrr76qRYsWqWLFinrhhRf0ww8/6LfHuS1dulQ9evTIdekoSV26dNHPP/+sy5cvOyI+nITi0cP06dNH27dv1/Tp03XkyBGtWLFCzz77bPYf4s2bN9WyZUt9+umn972PMWPGaPHixZo7d642btyoGzduqHv37srMzHTWZnikrKwsjRgxQsOHD7/n7zMzM+Xn56c//vGP6tatm5PTeaaHzXTz5s2qX7++Fi1apAMHDujll1/WSy+9pDlz5jg5qWd52FyLFCmiESNGaM2aNTp8+LA+++wzTZ8+Xe+++66Tk3qWh831jtu3b2vgwIFq27atk5J5rtzONDo6WvHx8dm3Dh06OCmhZ8rNXMeNG6evvvpKEydO1E8//aSVK1eyzz7Ew+b6+uuv59hP4+Pj1a5dO4WFhals2bJOTusZHjbTkydPqlevXmrTpo327NmjdevW6datW+ratauTk3qWh8111apVGjx4sF566SUdOHBAX331laZMmaIvvvjCyUndh73vsW7evKkuXbrIMAzFxMTohx9+0O3bt9WjRw9lZWU5c1MAlyhSpIh69+6t5cuX69ChQ3riiSf04osvqlatWvr44491+PBhFStWLF8XpenZs+c9y354Dh9XB0DuXbt2TRs3btTatWvVsWNHSVKVKlXUrFmz7GWGDRsm6ddzItzL9evXNX36dNlsNnXu3FmSFBUVpSpVqmjdunV6+umnHbwVnutf//qXJGny5Mlas2bNXb8PCAjQ//7v/0r69ZDza9euOTOeR3rYTCdMmJDj3y+//LLWr1+vxYsXa/DgwU7J6IkeNtcaNWrkOFdKlSpVFBcXp40bNzotoyd62FzvePPNN9WgQQO1a9dO33//vbPieaTczjQ4OFjly5d3ViyP97C5Hj58WP/617+0f/9+1a5dO/vnjRo1clpGT/SwuQYGBuY4yvns2bPauHGjoqKinJbR0zxsprt27VJ6ero+/vjj7IsXvP322+rQoYMSExNVunRpp+b1FA+ba1RUlHr06KFRo0ZJkqpVq6a3335bEydO1CuvvHLfc64VVGa8x/rhhx908uRJ7dy5M7tYmTlzph555BHFxsaqU6dODt4KwH2EhITojTfe0Pjx47Vt2zbZbDYNGTJEW7Zsydf9eXl5yWKxKDMzM18XsoHrccSjB7nzgvbbb79Vampqvu7jzgu4Ll26ZP+sUqVKql27tjZv3mxWVMBhbty4ka9PynB/x44dU3R0tNq1a+fqKB7vu+++04oVK/T555+7OkqB0rt3b5UtW1atWrXSokWLXB3H4y1btkzVqlVTdHS0qlWrpqpVq2rEiBGF7uvAjjZ9+nSVLFlSffr0cXUUj9W0aVP5+vpq2rRpyszMVFJSkmbOnKlmzZpROtohLS1Nfn5+OX7m7++vc+fO6fTp0y5K5TpmvMdKS0uTxWLJMVc/Pz95eXlp06ZNZkUFPIrFYlFoaKi+/vprvfHGG/L19c33fYWFhWnDhg0mpoMzccSjB/Hx8dGMGTM0cuRITZ06VY0aNVKrVq3Ur18/NW/ePFf3kZCQIG9v77terJUrV04JCQmOiO0RoqOj7zoXG1+LsI8jZrpixYrsr68UVmbOtWXLltq9e7fS0tI0cuRI/f3vfzcjokcyY67x8fEaOXKklixZoqCgIDPjeSQzZhoYGKjJkyerVatW8vHx0bfffqsBAwZo5syZGjp0qJlxPYYZcz1x4oROnz6tefPmacaMGbJYLHr99dfVo0cPbdmyRV5ehe9zabOfs7KyshQZGanhw4eraNGi9sbzSGbMtGrVqlq7dq369eunV155RVlZWWrUqJFWrVplZlSPYsZcn376ab366qtas2aNOnXqpGPHjumf//ynpF+fyx52HuOCxoz3WKGhoQoMDNT48eM1ceJESdJbb72lzMxMxcfHOzI+4PYyMzPvebGZvChVqpSSk5NNSgRnK3yvLD1cnz59dP78eS1fvlzPPvusNm/erNDQULsLA8MwCt3XKn6rbdu22rt3b44b5xG0j9kz/eGHHzR48GB9/vnneuqpp0xM6lnMnOv8+fO1e/duzZkzRytXrsx+oVwYmTHXoUOH6uWXX1ZoaKiDUnoWM2ZaunRpjRs3TqGhoWratKn+8pe/6L/+6780adIkB6V2f2bMNSsrS2lpaYqKilLbtm3Vpk0bRUVFafv27dqxY4eDkrs3s5+zVq1apbNnz+rFF180MaVnMWOmCQkJeuGFFzR8+HDt2LFDcXFxCgoKUv/+/QvtB8RmzHXkyJEaPXq0evXqpSJFiig0NFQDBw6UpEL7NUZ732OVKVNGCxcu1KpVqxQUFKQSJUro2rVraty4caGdKXBHUlKSKRdc/O2FauBZOOLRA/n5+alz587q3Lmz3nvvPb344ov64IMP9Prrr6tIkSIPXLd8+fLKzMxUYmKiypQpk/3zixcvFuqTyhcrVizHOe8kcY5GO5k5002bNqlr1676y1/+opdfftmEdJ7LzLlWqlRJklSnTh1lZmbqxRdf1Pjx4+XjU/ieGsyYa2xsrL7//nt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      "text/plain": [
       "<Figure size 1296x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_map(room_graph_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H10': [40, 40],\n",
       " 'H11': [50, 40],\n",
       " 'H12': [60, 40],\n",
       " 'H13': [70, 40],\n",
       " 'H14': [80, 40],\n",
       " 'H15': [90, 40],\n",
       " 'H16': [100, 40],\n",
       " 'H17': [110, 40],\n",
       " 'H18': [120, 40],\n",
       " 'H19': [130, 40],\n",
       " 'S10': [40, 40],\n",
       " 'R11': [50, 50],\n",
       " 'R12': [60, 50],\n",
       " 'R13': [70, 50],\n",
       " 'R14': [80, 50],\n",
       " 'R15': [90, 50],\n",
       " 'R16': [100, 50],\n",
       " 'R17': [110, 50],\n",
       " 'R18': [120, 50],\n",
       " 'R19': [130, 50],\n",
       " 'S19': [140, 40],\n",
       " 'EXIT': [165, 30],\n",
       " 'H20': [40, 65],\n",
       " 'H21': [50, 65],\n",
       " 'H22': [60, 65],\n",
       " 'H23': [70, 65],\n",
       " 'H24': [80, 65],\n",
       " 'H25': [90, 65],\n",
       " 'H26': [100, 65],\n",
       " 'H27': [110, 65],\n",
       " 'H28': [120, 65],\n",
       " 'H29': [130, 65],\n",
       " 'S20': [40, 65],\n",
       " 'R21': [50, 75],\n",
       " 'R22': [60, 75],\n",
       " 'R23': [70, 75],\n",
       " 'R24': [80, 75],\n",
       " 'R25': [90, 75],\n",
       " 'R26': [100, 75],\n",
       " 'R27': [110, 75],\n",
       " 'R28': [120, 75],\n",
       " 'R29': [130, 75],\n",
       " 'S29': [140, 65],\n",
       " 'H30': [40, 90],\n",
       " 'H31': [50, 90],\n",
       " 'H32': [60, 90],\n",
       " 'H33': [70, 90],\n",
       " 'H34': [80, 90],\n",
       " 'H35': [90, 90],\n",
       " 'H36': [100, 90],\n",
       " 'H37': [110, 90],\n",
       " 'H38': [120, 90],\n",
       " 'H39': [130, 90],\n",
       " 'S30': [40, 90],\n",
       " 'R31': [50, 100],\n",
       " 'R32': [60, 100],\n",
       " 'R33': [70, 100],\n",
       " 'R34': [80, 100],\n",
       " 'R35': [90, 100],\n",
       " 'R36': [100, 100],\n",
       " 'R37': [110, 100],\n",
       " 'R38': [120, 100],\n",
       " 'R39': [130, 100],\n",
       " 'S39': [140, 90],\n",
       " 'H40': [40, 115],\n",
       " 'H41': [50, 115],\n",
       " 'H42': [60, 115],\n",
       " 'H43': [70, 115],\n",
       " 'H44': [80, 115],\n",
       " 'H45': [90, 115],\n",
       " 'H46': [100, 115],\n",
       " 'H47': [110, 115],\n",
       " 'H48': [120, 115],\n",
       " 'H49': [130, 115],\n",
       " 'S40': [40, 115],\n",
       " 'R41': [50, 125],\n",
       " 'R42': [60, 125],\n",
       " 'R43': [70, 125],\n",
       " 'R44': [80, 125],\n",
       " 'R45': [90, 125],\n",
       " 'R46': [100, 125],\n",
       " 'R47': [110, 125],\n",
       " 'R48': [120, 125],\n",
       " 'R49': [130, 125],\n",
       " 'S49': [140, 115]}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xnode_label_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">show_map</span><span class=\"p\">(</span><span class=\"n\">graph_data</span><span class=\"p\">,</span> <span class=\"n\">node_colors</span><span class=\"o\">=</span><span class=\"kc\">None</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">G</span> <span class=\"o\">=</span> <span class=\"n\">nx</span><span class=\"o\">.</span><span class=\"n\">Graph</span><span class=\"p\">(</span><span class=\"n\">graph_data</span><span class=\"p\">[</span><span class=\"s1\">&#39;graph_dict&#39;</span><span class=\"p\">])</span>\n",
       "    <span class=\"n\">node_colors</span> <span class=\"o\">=</span> <span class=\"n\">node_colors</span> <span class=\"ow\">or</span> <span class=\"n\">graph_data</span><span class=\"p\">[</span><span class=\"s1\">&#39;node_colors&#39;</span><span class=\"p\">]</span>\n",
       "    <span class=\"n\">node_positions</span> <span class=\"o\">=</span> <span class=\"n\">graph_data</span><span class=\"p\">[</span><span class=\"s1\">&#39;node_positions&#39;</span><span class=\"p\">]</span>\n",
       "    <span class=\"n\">node_label_pos</span> <span class=\"o\">=</span> <span class=\"n\">graph_data</span><span class=\"p\">[</span><span class=\"s1\">&#39;node_label_positions&#39;</span><span class=\"p\">]</span>\n",
       "    <span class=\"n\">edge_weights</span> <span class=\"o\">=</span> <span class=\"n\">graph_data</span><span class=\"p\">[</span><span class=\"s1\">&#39;edge_weights&#39;</span><span class=\"p\">]</span>\n",
       "\n",
       "    <span class=\"c1\"># set the size of the plot</span>\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">figure</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">18</span><span class=\"p\">,</span> <span class=\"mi\">13</span><span class=\"p\">))</span>\n",
       "    <span class=\"c1\"># draw the graph (both nodes and edges) with locations from romania_locations</span>\n",
       "    <span class=\"n\">nx</span><span class=\"o\">.</span><span class=\"n\">draw</span><span class=\"p\">(</span><span class=\"n\">G</span><span class=\"p\">,</span> <span class=\"n\">pos</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"n\">k</span><span class=\"p\">:</span> <span class=\"n\">node_positions</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"k\">for</span> <span class=\"n\">k</span> <span class=\"ow\">in</span> <span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">nodes</span><span class=\"p\">()},</span>\n",
       "            <span class=\"n\">node_color</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"n\">node_colors</span><span class=\"p\">[</span><span class=\"n\">node</span><span class=\"p\">]</span> <span class=\"k\">for</span> <span class=\"n\">node</span> <span class=\"ow\">in</span> <span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">nodes</span><span class=\"p\">()],</span> <span class=\"n\">linewidths</span><span class=\"o\">=</span><span class=\"mf\">0.3</span><span class=\"p\">,</span> <span class=\"n\">edgecolors</span><span class=\"o\">=</span><span class=\"s1\">&#39;k&#39;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># draw labels for nodes</span>\n",
       "    <span class=\"n\">node_label_handles</span> <span class=\"o\">=</span> <span class=\"n\">nx</span><span class=\"o\">.</span><span class=\"n\">draw_networkx_labels</span><span class=\"p\">(</span><span class=\"n\">G</span><span class=\"p\">,</span> <span class=\"n\">pos</span><span class=\"o\">=</span><span class=\"n\">node_label_pos</span><span class=\"p\">,</span> <span class=\"n\">font_size</span><span class=\"o\">=</span><span class=\"mi\">14</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># add a white bounding box behind the node labels</span>\n",
       "    <span class=\"p\">[</span><span class=\"n\">label</span><span class=\"o\">.</span><span class=\"n\">set_bbox</span><span class=\"p\">(</span><span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">facecolor</span><span class=\"o\">=</span><span class=\"s1\">&#39;white&#39;</span><span class=\"p\">,</span> <span class=\"n\">edgecolor</span><span class=\"o\">=</span><span class=\"s1\">&#39;none&#39;</span><span class=\"p\">))</span> <span class=\"k\">for</span> <span class=\"n\">label</span> <span class=\"ow\">in</span> <span class=\"n\">node_label_handles</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">()]</span>\n",
       "\n",
       "    <span class=\"c1\"># add edge lables to the graph</span>\n",
       "    <span class=\"n\">nx</span><span class=\"o\">.</span><span class=\"n\">draw_networkx_edge_labels</span><span class=\"p\">(</span><span class=\"n\">G</span><span class=\"p\">,</span> <span class=\"n\">pos</span><span class=\"o\">=</span><span class=\"n\">node_positions</span><span class=\"p\">,</span> <span class=\"n\">edge_labels</span><span class=\"o\">=</span><span class=\"n\">edge_weights</span><span class=\"p\">,</span> <span class=\"n\">font_size</span><span class=\"o\">=</span><span class=\"mi\">14</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># add a legend</span>\n",
       "    <span class=\"n\">white_circle</span> <span class=\"o\">=</span> <span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">Line2D</span><span class=\"p\">([],</span> <span class=\"p\">[],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s2\">&quot;white&quot;</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s1\">&#39;o&#39;</span><span class=\"p\">,</span> <span class=\"n\">markersize</span><span class=\"o\">=</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"n\">markerfacecolor</span><span class=\"o\">=</span><span class=\"s2\">&quot;white&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">orange_circle</span> <span class=\"o\">=</span> <span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">Line2D</span><span class=\"p\">([],</span> <span class=\"p\">[],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s2\">&quot;orange&quot;</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s1\">&#39;o&#39;</span><span class=\"p\">,</span> <span class=\"n\">markersize</span><span class=\"o\">=</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"n\">markerfacecolor</span><span class=\"o\">=</span><span class=\"s2\">&quot;orange&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">red_circle</span> <span class=\"o\">=</span> <span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">Line2D</span><span class=\"p\">([],</span> <span class=\"p\">[],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s2\">&quot;red&quot;</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s1\">&#39;o&#39;</span><span class=\"p\">,</span> <span class=\"n\">markersize</span><span class=\"o\">=</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"n\">markerfacecolor</span><span class=\"o\">=</span><span class=\"s2\">&quot;red&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">gray_circle</span> <span class=\"o\">=</span> <span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">Line2D</span><span class=\"p\">([],</span> <span class=\"p\">[],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s2\">&quot;gray&quot;</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s1\">&#39;o&#39;</span><span class=\"p\">,</span> <span class=\"n\">markersize</span><span class=\"o\">=</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"n\">markerfacecolor</span><span class=\"o\">=</span><span class=\"s2\">&quot;gray&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">green_circle</span> <span class=\"o\">=</span> <span class=\"n\">lines</span><span class=\"o\">.</span><span class=\"n\">Line2D</span><span class=\"p\">([],</span> <span class=\"p\">[],</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s2\">&quot;green&quot;</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s1\">&#39;o&#39;</span><span class=\"p\">,</span> <span class=\"n\">markersize</span><span class=\"o\">=</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"n\">markerfacecolor</span><span class=\"o\">=</span><span class=\"s2\">&quot;green&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">((</span><span class=\"n\">white_circle</span><span class=\"p\">,</span> <span class=\"n\">orange_circle</span><span class=\"p\">,</span> <span class=\"n\">red_circle</span><span class=\"p\">,</span> <span class=\"n\">gray_circle</span><span class=\"p\">,</span> <span class=\"n\">green_circle</span><span class=\"p\">),</span>\n",
       "               <span class=\"p\">(</span><span class=\"s1\">&#39;Un-explored&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Frontier&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Currently Exploring&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Explored&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;Final Solution&#39;</span><span class=\"p\">),</span>\n",
       "               <span class=\"n\">numpoints</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">prop</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;size&#39;</span><span class=\"p\">:</span> <span class=\"mi\">16</span><span class=\"p\">},</span> <span class=\"n\">loc</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mf\">.8</span><span class=\"p\">,</span> <span class=\"mf\">.75</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"c1\"># show the plot. No need to use in notebooks. nx.draw will show the graph itself.</span>\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">()</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "psource(show_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_breadth_search_for_vis(problem):\n",
    "    \"\"\"Search through the successors of a problem to find a goal.\n",
    "    The argument frontier should be an empty queue.\n",
    "    Don't worry about repeated paths to a state. [Figure 3.7]\"\"\"\n",
    "    \n",
    "    # we use these two variables at the time of visualisations\n",
    "    iterations = 0\n",
    "    all_node_colors = []\n",
    "    node_colors = {k : 'white' for k in problem.graph.nodes()}\n",
    "    \n",
    "    #Adding first node to the queue\n",
    "    frontier = deque([Node(problem.initial)])\n",
    "    \n",
    "    node_colors[Node(problem.initial).state] = \"orange\"\n",
    "    iterations += 1\n",
    "    all_node_colors.append(dict(node_colors))\n",
    "    \n",
    "    while frontier:\n",
    "        #Popping first node of queue\n",
    "        node = frontier.popleft()\n",
    "        \n",
    "        # modify the currently searching node to red\n",
    "        node_colors[node.state] = \"red\"\n",
    "        iterations += 1\n",
    "        all_node_colors.append(dict(node_colors))\n",
    "        \n",
    "        if problem.goal_test(node.state):\n",
    "            # modify goal node to green after reaching the goal\n",
    "            node_colors[node.state] = \"green\"\n",
    "            iterations += 1\n",
    "            all_node_colors.append(dict(node_colors))\n",
    "            return(iterations, all_node_colors, node)\n",
    "        \n",
    "        frontier.extend(node.expand(problem))\n",
    "           \n",
    "        for n in node.expand(problem):\n",
    "            node_colors[n.state] = \"orange\"\n",
    "            iterations += 1\n",
    "            all_node_colors.append(dict(node_colors))\n",
    "\n",
    "        # modify the color of explored nodes to gray\n",
    "        node_colors[node.state] = \"gray\"\n",
    "        iterations += 1\n",
    "        all_node_colors.append(dict(node_colors))\n",
    "        \n",
    "    return None\n",
    "\n",
    "def breadth_first_tree_search(problem):\n",
    "    \"Search the shallowest nodes in the search tree first.\"\n",
    "    iterations, all_node_colors, node = tree_breadth_search_for_vis(problem)\n",
    "    return(iterations, all_node_colors, node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58e47ca6772a47658aad2f1e10b2c49c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5246592a7fbb44ca92afc03b22563bb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_node_colors = []\n",
    "room_problem = GraphProblem('R18', 'EXIT', room_map)\n",
    "a, b, c = breadth_first_tree_search(room_problem)\n",
    "display_visual(room_graph_data, user_input=False, \n",
    "               algorithm=breadth_first_tree_search, \n",
    "               problem=room_problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_depth_search_for_vis(problem):\n",
    "    \"\"\"Search through the successors of a problem to find a goal.\n",
    "    The argument frontier should be an empty queue.\n",
    "    Don't worry about repeated paths to a state. [Figure 3.7]\"\"\"\n",
    "    \n",
    "    # we use these two variables at the time of visualisations\n",
    "    iterations = 0\n",
    "    all_node_colors = []\n",
    "    node_colors = {k : 'white' for k in problem.graph.nodes()}\n",
    "    \n",
    "    #Adding first node to the stack\n",
    "    frontier = [Node(problem.initial)]\n",
    "    \n",
    "    node_colors[Node(problem.initial).state] = \"orange\"\n",
    "    iterations += 1\n",
    "    all_node_colors.append(dict(node_colors))\n",
    "    \n",
    "    while frontier:\n",
    "        #Popping first node of stack\n",
    "        node = frontier.pop()\n",
    "        \n",
    "        # modify the currently searching node to red\n",
    "        node_colors[node.state] = \"red\"\n",
    "        iterations += 1\n",
    "        all_node_colors.append(dict(node_colors))\n",
    "        \n",
    "        if problem.goal_test(node.state):\n",
    "            # modify goal node to green after reaching the goal\n",
    "            node_colors[node.state] = \"green\"\n",
    "            iterations += 1\n",
    "            all_node_colors.append(dict(node_colors))\n",
    "            return(iterations, all_node_colors, node)\n",
    "        \n",
    "        frontier.extend(node.expand(problem))\n",
    "           \n",
    "        for n in node.expand(problem):\n",
    "            node_colors[n.state] = \"orange\"\n",
    "            iterations += 1\n",
    "            all_node_colors.append(dict(node_colors))\n",
    "\n",
    "        # modify the color of explored nodes to gray\n",
    "        node_colors[node.state] = \"gray\"\n",
    "        iterations += 1\n",
    "        all_node_colors.append(dict(node_colors))\n",
    "        \n",
    "    return None\n",
    "\n",
    "def depth_first_tree_search(problem):\n",
    "    \"Search the deepest nodes in the search tree first.\"\n",
    "    iterations, all_node_colors, node = tree_depth_search_for_vis(problem)\n",
    "    return(iterations, all_node_colors, node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2c86e9068064c0c89a2566855cb49e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='iteration', max=1), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e83ebd200b78495981423aa8964aa1ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButton(value=False, description='visualize'), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_node_colors = []\n",
    "room_problem = GraphProblem('R35', 'EXIT', room_map)\n",
    "a, b, c = breadth_first_tree_search(room_problem)\n",
    "display_visual(room_graph_data, user_input=False, \n",
    "               algorithm=depth_first_tree_search, \n",
    "               problem=room_problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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