{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notebook import *\n",
    "from search import *\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from search import *\n",
    "from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens\n",
    "\n",
    "# Needed to hide warnings in the matplotlib sections\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "room_map = UndirectedGraph(dict(\n",
    "    H10=dict(H11=5,S10=5),\n",
    "    H11=dict(H10=5,R11=5,H12=5),\n",
    "    H12=dict(H11=5,R12=5,H13=5),\n",
    "    H13=dict(H12=5,R13=5,H14=5),\n",
    "    H14=dict(H13=5,R14=5,H15=5),\n",
    "    H15=dict(H14=5,H16=5),\n",
    "    H16=dict(H15=5,R16=5,H17=5),\n",
    "    H17=dict(H16=5,R17=5,H18=5),\n",
    "    H18=dict(H17=5,R18=5,H19=5),\n",
    "    H19=dict(H18=5,S19=5),\n",
    "    S19=dict(H19=5,S29=10,EXIT=10),\n",
    "\n",
    "    EXIT=dict(S19=10),\n",
    "    S10=dict(H10=5,S20=10),\n",
    "    R11=dict(H11=5),\n",
    "    R12=dict(H12=5,R13=5),\n",
    "    R13=dict(H13=5,R12=5),\n",
    "    R14=dict(H14=5,R15=5),\n",
    "    R15=dict(H15=5,R14=5,R16=5),\n",
    "    R16=dict(H16=5,R15=5),\n",
    "    R17=dict(H17=5),\n",
    "    R18=dict(H18=5),\n",
    "    R19=dict(H19=5),\n",
    "\n",
    "    H20=dict(H21=5,S20=5),\n",
    "    H21=dict(H20=5,R21=5,H22=5),\n",
    "    H22=dict(H21=5,R22=5,H23=5),\n",
    "    H23=dict(H22=5,R23=5,H24=5),\n",
    "    H24=dict(H23=5,R24=5,H25=5),\n",
    "    H25=dict(H24=5,H26=5),\n",
    "    H26=dict(H25=5,R26=5,H27=5),\n",
    "    H27=dict(H26=5,R27=5,H28=5),\n",
    "    H28=dict(H27=5,R28=5,H29=5),\n",
    "    H29=dict(H28=5,S29=5),\n",
    "    S29=dict(H29=5,S19=10,S39=10),\n",
    "\n",
    "    S20=dict(H20=5,S10=5,S30=10),\n",
    "    R21=dict(H21=5),\n",
    "    R22=dict(H22=5,R23=5),\n",
    "    R23=dict(H23=5,R22=5),\n",
    "    R24=dict(H24=5,R25=5),\n",
    "    R25=dict(H25=5,R24=5,R26=5),\n",
    "    R26=dict(H26=5,R25=5),\n",
    "    R27=dict(H27=5),\n",
    "    R28=dict(H28=5),\n",
    "    R29=dict(H29=5),\n",
    "\n",
    "    H30=dict(H31=5,S30=5),\n",
    "    H31=dict(H30=5,R31=5,H32=5),\n",
    "    H32=dict(H31=5,R32=5,H33=5),\n",
    "    H33=dict(H32=5,R33=5,H34=5),\n",
    "    H34=dict(H33=5,R34=5,H35=5),\n",
    "    H35=dict(H34=5,H36=5),\n",
    "    H36=dict(H35=5,R36=5,H37=5),\n",
    "    H37=dict(H36=5,R37=5,H38=5),\n",
    "    H38=dict(H37=5,R38=5,H39=5),\n",
    "    H39=dict(H38=5,S39=5),\n",
    "    S39=dict(H39=5,S29=10,S49=10),\n",
    "\n",
    "    S30=dict(H30=5,S20=10),\n",
    "    R31=dict(H31=5),\n",
    "    R32=dict(H32=5,R33=5),\n",
    "    R33=dict(H33=5,R32=5),\n",
    "    R34=dict(H34=5,R35=5),\n",
    "    R35=dict(H35=5,R34=5,R36=5),\n",
    "    R36=dict(H36=5,R35=5),\n",
    "    R37=dict(H37=5),\n",
    "    R38=dict(H38=5),\n",
    "    R39=dict(H39=5),\n",
    "\n",
    "    H40=dict(H41=5,S40=5),\n",
    "    H41=dict(H40=5,R41=5,H42=5),\n",
    "    H42=dict(H41=5,R42=5,H43=5),\n",
    "    H43=dict(H42=5,R43=5,H44=5),\n",
    "    H44=dict(H43=5,R44=5,H45=5),\n",
    "    H45=dict(H44=5,H46=5),\n",
    "    H46=dict(H45=5,R46=5,H47=5),\n",
    "    H47=dict(H46=5,R47=5,H48=5),\n",
    "    H48=dict(H47=5,R48=5,H49=5),\n",
    "    H49=dict(H48=5,S49=5),\n",
    "    S49=dict(H49=5,S39=10),\n",
    "\n",
    "    S40=dict(H40=5,S30=10),\n",
    "    R41=dict(H41=5),\n",
    "    R42=dict(H42=5,R43=5),\n",
    "    R43=dict(H43=5,R42=5),\n",
    "    R44=dict(H44=5,R45=5),\n",
    "    R45=dict(H45=5,R44=5,R46=5),\n",
    "    R46=dict(H46=5,R45=5),\n",
    "    R47=dict(H47=5),\n",
    "    R48=dict(H48=5),\n",
    "    R49=dict(H49=5)\n",
    "    \n",
    "    ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "room_map.locations = dict(\n",
    "    H10=(40,50),\n",
    "    H11=(50,50),\n",
    "    H12=(60,50),\n",
    "    H13=(70,50),\n",
    "    H14=(80,50),\n",
    "    H15=(90,50),\n",
    "    H16=(100,50),\n",
    "    H17=(110,50),\n",
    "    H18=(120,50),\n",
    "    H19=(130,50),\n",
    "\n",
    "    S10=(40,50),\n",
    "    R11=(50,60),\n",
    "    R12=(60,60),\n",
    "    R13=(70,60),\n",
    "    R14=(80,60),\n",
    "    R15=(90,60),\n",
    "    R16=(100,60),\n",
    "    R17=(110,60),\n",
    "    R18=(120,60),\n",
    "    R19=(130,60),\n",
    "    S19=(140,50),\n",
    "    EXIT=(165,40),\n",
    "\n",
    "\n",
    "    H20=(40,75),\n",
    "    H21=(50,75),\n",
    "    H22=(60,75),\n",
    "    H23=(70,75),\n",
    "    H24=(80,75),\n",
    "    H25=(90,75),\n",
    "    H26=(100,75),\n",
    "    H27=(110,75),\n",
    "    H28=(120,75),\n",
    "    H29=(130,75),\n",
    "\n",
    "    S20=(40,75),\n",
    "    R21=(50,85),\n",
    "    R22=(60,85),\n",
    "    R23=(70,85),\n",
    "    R24=(80,85),\n",
    "    R25=(90,85),\n",
    "    R26=(100,85),\n",
    "    R27=(110,85),\n",
    "    R28=(120,85),\n",
    "    R29=(130,85),\n",
    "    S29=(140,75),\n",
    "\n",
    "    H30=(40,100),\n",
    "    H31=(50,100),\n",
    "    H32=(60,100),\n",
    "    H33=(70,100),\n",
    "    H34=(80,100),\n",
    "    H35=(90,100),\n",
    "    H36=(100,100),\n",
    "    H37=(110,100),\n",
    "    H38=(120,100),\n",
    "    H39=(130,100),\n",
    "\n",
    "    S30=(40,100),\n",
    "    R31=(50,110),\n",
    "    R32=(60,110),\n",
    "    R33=(70,110),\n",
    "    R34=(80,110),\n",
    "    R35=(90,110),\n",
    "    R36=(100,110),\n",
    "    R37=(110,110),\n",
    "    R38=(120,110),\n",
    "    R39=(130,110),\n",
    "    S39=(140,100),\n",
    "\n",
    "    \n",
    "    H40=(40,125),\n",
    "    H41=(50,125),\n",
    "    H42=(60,125),\n",
    "    H43=(70,125),\n",
    "    H44=(80,125),\n",
    "    H45=(90,125),\n",
    "    H46=(100,125),\n",
    "    H47=(110,125),\n",
    "    H48=(120,125),\n",
    "    H49=(130,125),\n",
    "\n",
    "    S40=(40,125),\n",
    "    R41=(50,135),\n",
    "    R42=(60,135),\n",
    "    R43=(70,135),\n",
    "    R44=(80,135),\n",
    "    R45=(90,135),\n",
    "    R46=(100,135),\n",
    "    R47=(110,135),\n",
    "    R48=(120,135),\n",
    "    R49=(130,135),\n",
    "    S49=(140,125)\n",
    "\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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]-2]  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",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'H10': [40, 48],\n",
       " 'H11': [50, 48],\n",
       " 'H12': [60, 48],\n",
       " 'H13': [70, 48],\n",
       " 'H14': [80, 48],\n",
       " 'H15': [90, 48],\n",
       " 'H16': [100, 48],\n",
       " 'H17': [110, 48],\n",
       " 'H18': [120, 48],\n",
       " 'H19': [130, 48],\n",
       " 'S10': [40, 48],\n",
       " 'R11': [50, 58],\n",
       " 'R12': [60, 58],\n",
       " 'R13': [70, 58],\n",
       " 'R14': [80, 58],\n",
       " 'R15': [90, 58],\n",
       " 'R16': [100, 58],\n",
       " 'R17': [110, 58],\n",
       " 'R18': [120, 58],\n",
       " 'R19': [130, 58],\n",
       " 'S19': [140, 48],\n",
       " 'EXIT': [165, 38],\n",
       " 'H20': [40, 73],\n",
       " 'H21': [50, 73],\n",
       " 'H22': [60, 73],\n",
       " 'H23': [70, 73],\n",
       " 'H24': [80, 73],\n",
       " 'H25': [90, 73],\n",
       " 'H26': [100, 73],\n",
       " 'H27': [110, 73],\n",
       " 'H28': [120, 73],\n",
       " 'H29': [130, 73],\n",
       " 'S20': [40, 73],\n",
       " 'R21': [50, 83],\n",
       " 'R22': [60, 83],\n",
       " 'R23': [70, 83],\n",
       " 'R24': [80, 83],\n",
       " 'R25': [90, 83],\n",
       " 'R26': [100, 83],\n",
       " 'R27': [110, 83],\n",
       " 'R28': [120, 83],\n",
       " 'R29': [130, 83],\n",
       " 'S29': [140, 73],\n",
       " 'H30': [40, 98],\n",
       " 'H31': [50, 98],\n",
       " 'H32': [60, 98],\n",
       " 'H33': [70, 98],\n",
       " 'H34': [80, 98],\n",
       " 'H35': [90, 98],\n",
       " 'H36': [100, 98],\n",
       " 'H37': [110, 98],\n",
       " 'H38': [120, 98],\n",
       " 'H39': [130, 98],\n",
       " 'S30': [40, 98],\n",
       " 'R31': [50, 108],\n",
       " 'R32': [60, 108],\n",
       " 'R33': [70, 108],\n",
       " 'R34': [80, 108],\n",
       " 'R35': [90, 108],\n",
       " 'R36': [100, 108],\n",
       " 'R37': [110, 108],\n",
       " 'R38': [120, 108],\n",
       " 'R39': [130, 108],\n",
       " 'S39': [140, 98],\n",
       " 'H40': [40, 123],\n",
       " 'H41': [50, 123],\n",
       " 'H42': [60, 123],\n",
       " 'H43': [70, 123],\n",
       " 'H44': [80, 123],\n",
       " 'H45': [90, 123],\n",
       " 'H46': [100, 123],\n",
       " 'H47': [110, 123],\n",
       " 'H48': [120, 123],\n",
       " 'H49': [130, 123],\n",
       " 'S40': [40, 123],\n",
       " 'R41': [50, 133],\n",
       " 'R42': [60, 133],\n",
       " 'R43': [70, 133],\n",
       " 'R44': [80, 133],\n",
       " 'R45': [90, 133],\n",
       " 'R46': [100, 133],\n",
       " 'R47': [110, 133],\n",
       " 'R48': [120, 133],\n",
       " 'R49': [130, 133],\n",
       " 'S49': [140, 123]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xnode_label_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<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=\"bp\">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=\"o\">.</span><span class=\"mi\">8</span><span class=\"p\">,</span><span class=\"o\">.</span><span class=\"mi\">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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee99c7cffe2f4d5d8f885d527ca2746c",
       "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": "9534af2b7304468da07fcd89d4dee75a",
       "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=breadth_first_tree_search, \n",
    "               problem=room_problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b38b32787e854a26ba01383161a2033e",
       "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": "97b3eed4494d4b10b53b93b266c13268",
       "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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