first commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: London Undergound Route Finder
+emoji: 📉
+colorFrom: gray
+colorTo: blue
+sdk: gradio
+sdk_version: 3.40.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import numpy as np
+import gradio as gr
+from src.utils import Utils
+from swiplserver import PrologMQI
+import plotly.graph_objects as go
+
+facts_path = "./src/facts.pl"
+search_algorithm_path = "./src/search_algorithms.pl"
+
+utils = Utils()
+possible_routes = []
+
+def query(origin, destination):
+    with PrologMQI() as mqi:
+        with mqi.create_thread() as prolog_thread:
+            result = prolog_thread.query(f'consult("{facts_path}").')
+            print(f"Loaded facts: {result}")
+            result = prolog_thread.query(f'consult("{search_algorithm_path}").')
+            print(f"Loaded rules: {result}")
+            print(f"Query Path: {origin} - - to - - {destination}")
+            prolog_thread.query_async(f"a_star_search({utils.remove_spl_chars(origin)}, {utils.remove_spl_chars(destination)}, Path).", find_all=False)
+            result = prolog_thread.query_async_result()
+            if (result == False) or (len(result) == 0):
+                return []
+            print(f"Total Results: {len(result)}")
+            routes = result[0]['Path']
+            print(f"Result route: {routes}")
+            return routes
+
+def load_map():
+    center_lat, center_lon = utils.get_map_center()
+    fig = go.Figure(layout=utils.get_default_map_layout(center_lat, center_lon))
+    fig.add_traces(utils.get_traces())
+    fig.add_trace(utils.get_markers())
+    fig = utils.filter_duplicate_traces(fig)
+    return fig
+
+def filter_map(origin, destination):
+    center_lat, center_lon = utils.get_map_center()
+    output_text = ""    
+    traces = []
+    if (len(origin) > 0) and (len(destination) > 0):
+        route = query(origin, destination)
+        if len(route) == 0:
+            output_text = "No Route Found"
+        else:
+            traces, center_lat, center_lon, output_text = utils.filter_marker_and_traces(route)
+    else:
+        traces = utils.get_traces()
+
+    fig = go.Figure(data=traces,layout=utils.get_default_map_layout(center_lat, center_lon))
+    fig = utils.filter_duplicate_traces(fig)
+    return fig, output_text
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Label(value="London Underground Stations Route Finder", label="")
+        map = gr.Plot(label="London Underground Station Map")
+        with gr.Row():
+            origin = gr.Dropdown(
+                choices=utils.get_all_station_names(), label="Origin 🔵")
+            destination = gr.Dropdown(
+                choices=utils.get_all_station_names(), label="Destination 🔴")
+        with gr.Row():
+            btn_clear = gr.Button(value="Clear")
+            btn_search = gr.Button(value="Search")
+            btn_clear.click(load_map, [], map)
+        output_label = gr.Label(label="Optimal Route")
+        btn_search.click(filter_map, [origin, destination], [map, output_label])
+    demo.load(load_map, [], map)
+
+demo.launch()

dataset/connections.csv

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+station1,station2,line,time
+49,87,1,1
+49,197,1,2
+84,148,1,3
+87,279,1,2
+148,279,1,1
+192,197,1,2
+192,212,1,2
+13,156,2,2
+13,250,2,2
+28,192,2,1
+48,126,2,1
+48,250,2,2
+126,259,2,2
+192,259,2,2
+2,156,3,2
+2,263,3,4
+14,92,3,1
+14,167,3,2
+25,161,3,1
+25,255,3,2
+44,161,3,1
+44,166,3,2
+87,255,3,2
+87,285,3,2
+90,104,3,2
+90,145,3,2
+92,145,3,4
+156,167,3,2
+166,263,3,2
+248,273,3,2
+248,285,3,2
+3,263,4,2
+3,156,6,4
+28,107,7,2
+107,285,7,3
+157,233,7,2
+233,279,7,1
+279,285,7,2
+7,145,9,2
+7,188,9,3
+13,157,9,2
+13,167,9,3
+29,84,9,1
+29,157,9,2
+49,151,9,2
+84,136,9,2
+89,145,9,2
+89,170,9,2
+89,277,9,1
+102,259,9,1
+102,277,9,2
+136,279,9,3
+151,259,9,1
+167,188,9,1
+60,126,10,1
+60,151,10,1
+107,133,10,2
+107,197,10,1
+126,223,10,2
+145,223,10,2
+151,197,10,2
+107,192,11,2
+107,273,11,2
+192,277,11,2
+198,272,11,1
+198,273,11,3
+13,279,12,4

dataset/lines.csv

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+line,name,colour,stripe
+1,Bakerloo Line,AE6017,
+3,Circle Line,FFE02B,
+6,Hammersmith & City Line,F491A8,
+7,Jubilee Line,949699,
+11,Victoria Line,0A9CDA,
+2,Central Line,F15B2E,
+4,District Line,00A166,
+5,East London Line,FBAE34,
+8,Metropolitan Line,91005A,
+9,Northern Line,000000,
+10,Piccadilly Line,094FA3,
+12,Waterloo & City Line,88D0C4,
+13,Docklands Light Railway,00A77E,FFFFFF

dataset/stations.csv

@@ -0,0 +1,51 @@
+id,latitude,longitude,name,display_name,zone,total_lines,rail,prolog_name
+2,51.5143,-0.0755,Aldgate,,1.0,2,0,aldgate
+3,51.5154,-0.0726,Aldgate East,Aldgate<br />East,1.0,2,0,aldgate_east
+7,51.5322,-0.1058,Angel,,1.0,1,0,angel
+13,51.5133,-0.0886,Bank,,1.0,4,0,bank
+14,51.5204,-0.0979,Barbican,,1.0,3,0,barbican
+25,51.512,-0.1031,Blackfriars,,1.0,2,0,blackfriars
+28,51.5142,-0.1494,Bond Street,Bond<br />Street,1.0,2,0,bond_street
+29,51.5011,-0.0943,Borough,,1.0,1,0,borough
+44,51.5113,-0.0904,Cannon Street,Cannon<br />Street,1.0,2,0,cannon_street
+48,51.5185,-0.1111,Chancery Lane,Chancery<br />Lane,1.0,1,0,chancery_lane
+49,51.508,-0.1247,Charing Cross,Charing<br />Cross,1.0,2,1,charing_cross
+60,51.5129,-0.1243,Covent Garden,Covent<br />Garden,1.0,1,0,covent_garden
+84,51.4943,-0.1001,Elephant & Castle,Elephant &<br />Castle,1.5,2,1,elephant_and_castle
+87,51.5074,-0.1223,Embankment,,1.0,4,0,embankment
+89,51.5282,-0.1337,Euston,,1.0,2,1,euston
+90,51.526,-0.1359,Euston Square,Euston<br />Square,1.0,3,0,euston_square
+92,51.5203,-0.1053,Farringdon,,1.0,3,1,farringdon
+102,51.5205,-0.1347,Goodge Street,Goodge<br />Street,1.0,1,0,goodge_street
+104,51.5238,-0.1439,Great Portland Street,Great<br />Portland<br />Street,1.0,3,0,great_portland_street
+107,51.5067,-0.1428,Green Park,Green<br />Park,1.0,3,0,green_park
+126,51.5174,-0.12,Holborn,,1.0,2,0,holborn
+133,51.5027,-0.1527,Hyde Park Corner,Hyde<br />Park<br />Corner,1.0,1,0,hyde_park_corner
+136,51.4884,-0.1053,Kennington,,2.0,1,0,kennington
+145,51.5308,-0.1238,King's Cross St. Pancras,King's Cross<br />St. Pancras,1.0,6,1,kings_cross_st_pancras
+148,51.4991,-0.1115,Lambeth North,Lambeth<br />North,1.0,1,0,lambeth_north
+151,51.5113,-0.1281,Leicester Square,Leicester<br />Square,1.0,2,0,leicester_square
+156,51.5178,-0.0823,Liverpool Street,Liverpool<br />Street,1.0,4,1,liverpool_street
+157,51.5052,-0.0864,London Bridge,London<br />Bridge,1.0,2,1,london_bridge
+161,51.5122,-0.094,Mansion House,Mansion<br />House,1.0,2,0,mansion_house
+166,51.5108,-0.0863,Monument,,1.0,2,0,monument
+167,51.5186,-0.0886,Moorgate,,1.0,4,1,moorgate
+170,51.5342,-0.1387,Mornington Crescent,Mornington<br />Crescent,2.0,1,0,mornington_crescent
+188,51.5263,-0.0873,Old Street,Old<br />Street,1.0,1,1,old_street
+192,51.515,-0.1415,Oxford Circus,Oxford<br />Circus,1.0,3,0,oxford_circus
+197,51.5098,-0.1342,Picadilly Circus,Picadilly<br />Circus,1.0,2,0,picadilly_circus
+198,51.4893,-0.1334,Pimlico,,1.0,1,0,pimlico
+212,51.5234,-0.1466,Regent's Park,Regent's<br />Park,1.0,1,0,regents_park
+223,51.523,-0.1244,Russell Square,Russell<br />Square,1.0,1,0,russell_square
+248,51.4994,-0.1335,St. James's Park,St. James's<br />Park,1.0,2,0,st_jamess_park
+250,51.5146,-0.0973,St. Paul's,St. Paul's,1.0,1,0,st_pauls
+255,51.5111,-0.1141,Temple,,1.0,2,0,temple
+259,51.5165,-0.131,Tottenham Court Road,Tottenham<br />Court<br />Road,1.0,2,0,tottenham_court_road
+262,51.5106,-0.0743,Tower Gateway,Tower<br />Gateway,1.0,1,0,tower_gateway
+263,51.5098,-0.0766,Tower Hill,Tower<br />Hill,1.0,2,0,tower_hill
+272,51.4861,-0.1253,Vauxhall,,1.5,1,1,vauxhall
+273,51.4965,-0.1447,Victoria,,1.0,3,1,victoria
+277,51.5247,-0.1384,Warren Street,Warren<br />Street,1.0,2,0,warren_street
+279,51.5036,-0.1143,Waterloo,,1.0,4,1,waterloo
+285,51.501,-0.1254,Westminster,,1.0,3,0,westminster
+233,51.501,-0.1052,Southwark,,1.0,1,0,southwark

prolog-assignment.ipynb

@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tubemaps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "london_stations = pd.read_csv(\"../dataset/tubemaps/london.stations.csv\")\n",
+    "london_connections = pd.read_csv(\"../dataset/tubemaps/london.connections.csv\")\n",
+    "london_lines = pd.read_csv(\"../dataset/tubemaps/london.lines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map Bounds to limit stations of interest\n",
+    "lat_max = 51.5345\n",
+    "lat_min = 51.4860\n",
+    "long_max = -0.0725\n",
+    "long_min = -0.1527\n",
+    "\n",
+    "london_stations_filtered = london_stations[(london_stations['latitude'] <= lat_max) & \n",
+    "                                        (london_stations['latitude'] >= lat_min) & \n",
+    "                                        (london_stations['longitude'] <= long_max) & \n",
+    "                                        (london_stations['longitude'] >= long_min)]\n",
+    "london_stations_filtered = london_stations_filtered.drop(columns=['Unnamed: 0']).drop_duplicates()\n",
+    "london_stations_filtered.to_csv(\"dataset/stations.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>station1</th>\n",
+       "      <th>station2</th>\n",
+       "      <th>line</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>49</td>\n",
+       "      <td>87</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>49</td>\n",
+       "      <td>197</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>84</td>\n",
+       "      <td>148</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>87</td>\n",
+       "      <td>279</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>148</td>\n",
+       "      <td>279</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>107</td>\n",
+       "      <td>273</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>192</td>\n",
+       "      <td>277</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>198</td>\n",
+       "      <td>272</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>198</td>\n",
+       "      <td>273</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>13</td>\n",
+       "      <td>279</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>67 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     station1  station2  line  time\n",
+       "2          49        87     1     1\n",
+       "3          49       197     1     2\n",
+       "6          84       148     1     3\n",
+       "7          87       279     1     2\n",
+       "16        148       279     1     1\n",
+       "..        ...       ...   ...   ...\n",
+       "398       107       273    11     2\n",
+       "400       192       277    11     2\n",
+       "401       198       272    11     1\n",
+       "402       198       273    11     3\n",
+       "405        13       279    12     4\n",
+       "\n",
+       "[67 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "london_connections_filtered =  london_connections[(london_connections['station1'].isin(london_stations_filtered['id'])) & (london_connections['station2'].isin(london_stations_filtered['id']))]\n",
+    "london_connections_filtered = london_connections_filtered.drop_duplicates(subset=['station1','station2'])\n",
+    "london_connections_filtered.to_csv(\"dataset/connections.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "london_lines.to_csv(\"dataset/lines.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DB Creation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "london_stations = pd.read_csv(\"dataset/stations.csv\")\n",
+    "london_connections = pd.read_csv(\"dataset/connections.csv\")\n",
+    "london_lines = pd.read_csv(\"dataset/lines.csv\")\n",
+    "\n",
+    "facts = \"\"\n",
+    "\n",
+    "def remove_spl_chars(text):\n",
+    "    return text.lower().replace(\" \",\"_\").replace(\"'\",\"\").replace(\"(\",\"_\").replace(\")\",\"_\").replace(\"&\",\"and\").replace(\".\",\"\").replace(\",\",\"\")\n",
+    "\n",
+    "# ETA\n",
+    "for station1, station2, line, time in london_connections.values.tolist():  \n",
+    "        station1_name = remove_spl_chars(london_stations[london_stations['id']==station1]['name'].values[0])\n",
+    "        station2_name = remove_spl_chars(london_stations[london_stations['id']==station2]['name'].values[0])\n",
+    "        facts += f\"eta({station1_name},{station2_name},{time}).\\n\"\n",
+    "        facts += f\"eta({station2_name},{station1_name},{time}).\\n\"\n",
+    "# facts += \"eta(Node, NextNode, Time):- !,eta(NextNode, Node, Time).\\n\"\n",
+    "for id, lat, long, name, display_name, zone, tot_line, rail, prolog_name in london_stations.values.tolist():  \n",
+    "        facts += f\"location({prolog_name},{lat},{long}).\\n\"\n",
+    "\n",
+    "text_file = open(\"src/facts.pl\", \"w\")\n",
+    "n = text_file.write(facts)\n",
+    "text_file.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "True\n",
+      "Path: temple - - to - - cannon_street\n",
+      "[{'Path': ['temple', 'blackfriars', 'mansion_house', 'cannon_street']}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from swiplserver import PrologMQI\n",
+    "\n",
+    "with PrologMQI() as mqi:\n",
+    "    with mqi.create_thread() as prolog_thread:\n",
+    "        result = prolog_thread.query(\"consult(\\\"./src/facts.pl\\\").\")\n",
+    "        print(result)\n",
+    "        result = prolog_thread.query(\"consult(\\\"./src/search_algorithms.pl\\\").\")\n",
+    "        print(result)\n",
+    "        origin = remove_spl_chars(\"Temple\")\n",
+    "        destination = remove_spl_chars(\"Cannon Street\")        \n",
+    "        print(f\"Path: {origin} - - to - - {destination}\")\n",
+    "        query = f\"breadth_first_search({origin}, {destination}, Path).\"\n",
+    "        result = prolog_thread.query_async(query, find_all=False)\n",
+    "        result = prolog_thread.query_async_result()\n",
+    "        print(result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,3 @@
+swiplserver
+gradio
+pandas

src/facts.pl

@@ -0,0 +1,184 @@
+eta(charing_cross,embankment,1).
+eta(embankment,charing_cross,1).
+eta(charing_cross,picadilly_circus,2).
+eta(picadilly_circus,charing_cross,2).
+eta(elephant_and_castle,lambeth_north,3).
+eta(lambeth_north,elephant_and_castle,3).
+eta(embankment,waterloo,2).
+eta(waterloo,embankment,2).
+eta(lambeth_north,waterloo,1).
+eta(waterloo,lambeth_north,1).
+eta(oxford_circus,picadilly_circus,2).
+eta(picadilly_circus,oxford_circus,2).
+eta(oxford_circus,regents_park,2).
+eta(regents_park,oxford_circus,2).
+eta(bank,liverpool_street,2).
+eta(liverpool_street,bank,2).
+eta(bank,st_pauls,2).
+eta(st_pauls,bank,2).
+eta(bond_street,oxford_circus,1).
+eta(oxford_circus,bond_street,1).
+eta(chancery_lane,holborn,1).
+eta(holborn,chancery_lane,1).
+eta(chancery_lane,st_pauls,2).
+eta(st_pauls,chancery_lane,2).
+eta(holborn,tottenham_court_road,2).
+eta(tottenham_court_road,holborn,2).
+eta(oxford_circus,tottenham_court_road,2).
+eta(tottenham_court_road,oxford_circus,2).
+eta(aldgate,liverpool_street,2).
+eta(liverpool_street,aldgate,2).
+eta(aldgate,tower_hill,4).
+eta(tower_hill,aldgate,4).
+eta(barbican,farringdon,1).
+eta(farringdon,barbican,1).
+eta(barbican,moorgate,2).
+eta(moorgate,barbican,2).
+eta(blackfriars,mansion_house,1).
+eta(mansion_house,blackfriars,1).
+eta(blackfriars,temple,2).
+eta(temple,blackfriars,2).
+eta(cannon_street,mansion_house,1).
+eta(mansion_house,cannon_street,1).
+eta(cannon_street,monument,2).
+eta(monument,cannon_street,2).
+eta(embankment,temple,2).
+eta(temple,embankment,2).
+eta(embankment,westminster,2).
+eta(westminster,embankment,2).
+eta(euston_square,great_portland_street,2).
+eta(great_portland_street,euston_square,2).
+eta(euston_square,kings_cross_st_pancras,2).
+eta(kings_cross_st_pancras,euston_square,2).
+eta(farringdon,kings_cross_st_pancras,4).
+eta(kings_cross_st_pancras,farringdon,4).
+eta(liverpool_street,moorgate,2).
+eta(moorgate,liverpool_street,2).
+eta(monument,tower_hill,2).
+eta(tower_hill,monument,2).
+eta(st_jamess_park,victoria,2).
+eta(victoria,st_jamess_park,2).
+eta(st_jamess_park,westminster,2).
+eta(westminster,st_jamess_park,2).
+eta(aldgate_east,tower_hill,2).
+eta(tower_hill,aldgate_east,2).
+eta(aldgate_east,liverpool_street,4).
+eta(liverpool_street,aldgate_east,4).
+eta(bond_street,green_park,2).
+eta(green_park,bond_street,2).
+eta(green_park,westminster,3).
+eta(westminster,green_park,3).
+eta(london_bridge,southwark,2).
+eta(southwark,london_bridge,2).
+eta(southwark,waterloo,1).
+eta(waterloo,southwark,1).
+eta(waterloo,westminster,2).
+eta(westminster,waterloo,2).
+eta(angel,kings_cross_st_pancras,2).
+eta(kings_cross_st_pancras,angel,2).
+eta(angel,old_street,3).
+eta(old_street,angel,3).
+eta(bank,london_bridge,2).
+eta(london_bridge,bank,2).
+eta(bank,moorgate,3).
+eta(moorgate,bank,3).
+eta(borough,elephant_and_castle,1).
+eta(elephant_and_castle,borough,1).
+eta(borough,london_bridge,2).
+eta(london_bridge,borough,2).
+eta(charing_cross,leicester_square,2).
+eta(leicester_square,charing_cross,2).
+eta(elephant_and_castle,kennington,2).
+eta(kennington,elephant_and_castle,2).
+eta(euston,kings_cross_st_pancras,2).
+eta(kings_cross_st_pancras,euston,2).
+eta(euston,mornington_crescent,2).
+eta(mornington_crescent,euston,2).
+eta(euston,warren_street,1).
+eta(warren_street,euston,1).
+eta(goodge_street,tottenham_court_road,1).
+eta(tottenham_court_road,goodge_street,1).
+eta(goodge_street,warren_street,2).
+eta(warren_street,goodge_street,2).
+eta(kennington,waterloo,3).
+eta(waterloo,kennington,3).
+eta(leicester_square,tottenham_court_road,1).
+eta(tottenham_court_road,leicester_square,1).
+eta(moorgate,old_street,1).
+eta(old_street,moorgate,1).
+eta(covent_garden,holborn,1).
+eta(holborn,covent_garden,1).
+eta(covent_garden,leicester_square,1).
+eta(leicester_square,covent_garden,1).
+eta(green_park,hyde_park_corner,2).
+eta(hyde_park_corner,green_park,2).
+eta(green_park,picadilly_circus,1).
+eta(picadilly_circus,green_park,1).
+eta(holborn,russell_square,2).
+eta(russell_square,holborn,2).
+eta(kings_cross_st_pancras,russell_square,2).
+eta(russell_square,kings_cross_st_pancras,2).
+eta(leicester_square,picadilly_circus,2).
+eta(picadilly_circus,leicester_square,2).
+eta(green_park,oxford_circus,2).
+eta(oxford_circus,green_park,2).
+eta(green_park,victoria,2).
+eta(victoria,green_park,2).
+eta(oxford_circus,warren_street,2).
+eta(warren_street,oxford_circus,2).
+eta(pimlico,vauxhall,1).
+eta(vauxhall,pimlico,1).
+eta(pimlico,victoria,3).
+eta(victoria,pimlico,3).
+eta(bank,waterloo,4).
+eta(waterloo,bank,4).
+location(aldgate,51.5143,-0.0755).
+location(aldgate_east,51.5154,-0.0726).
+location(angel,51.5322,-0.1058).
+location(bank,51.5133,-0.0886).
+location(barbican,51.5204,-0.0979).
+location(blackfriars,51.512,-0.1031).
+location(bond_street,51.5142,-0.1494).
+location(borough,51.5011,-0.0943).
+location(cannon_street,51.5113,-0.0904).
+location(chancery_lane,51.5185,-0.1111).
+location(charing_cross,51.508,-0.1247).
+location(covent_garden,51.5129,-0.1243).
+location(elephant_and_castle,51.4943,-0.1001).
+location(embankment,51.5074,-0.1223).
+location(euston,51.5282,-0.1337).
+location(euston_square,51.526,-0.1359).
+location(farringdon,51.5203,-0.1053).
+location(goodge_street,51.5205,-0.1347).
+location(great_portland_street,51.5238,-0.1439).
+location(green_park,51.5067,-0.1428).
+location(holborn,51.5174,-0.12).
+location(hyde_park_corner,51.5027,-0.1527).
+location(kennington,51.4884,-0.1053).
+location(kings_cross_st_pancras,51.5308,-0.1238).
+location(lambeth_north,51.4991,-0.1115).
+location(leicester_square,51.5113,-0.1281).
+location(liverpool_street,51.5178,-0.0823).
+location(london_bridge,51.5052,-0.0864).
+location(mansion_house,51.5122,-0.094).
+location(monument,51.5108,-0.0863).
+location(moorgate,51.5186,-0.0886).
+location(mornington_crescent,51.5342,-0.1387).
+location(old_street,51.5263,-0.0873).
+location(oxford_circus,51.515,-0.1415).
+location(picadilly_circus,51.5098,-0.1342).
+location(pimlico,51.4893,-0.1334).
+location(regents_park,51.5234,-0.1466).
+location(russell_square,51.523,-0.1244).
+location(st_jamess_park,51.4994,-0.1335).
+location(st_pauls,51.5146,-0.0973).
+location(temple,51.5111,-0.1141).
+location(tottenham_court_road,51.5165,-0.131).
+location(tower_gateway,51.5106,-0.0743).
+location(tower_hill,51.5098,-0.0766).
+location(vauxhall,51.4861,-0.1253).
+location(victoria,51.4965,-0.1447).
+location(warren_street,51.5247,-0.1384).
+location(waterloo,51.5036,-0.1143).
+location(westminster,51.501,-0.1254).
+location(southwark,51.501,-0.1052).

src/search_algorithms.pl

@@ -0,0 +1,96 @@
+% neighbor/2 predicate to define neighbors (replace with your own logic)
+neighbor(Node, Neighbor) :- eta(Node, Neighbor, _).
+neighbor(Node, Neighbor) :- eta(Neighbor, Node, _).
+
+get_cost(Node, Neighbor, Cost) :- eta(Node, Neighbor, Cost).
+get_cost(Node, Neighbor,Cost) :- eta(Neighbor, Node, Cost).
+
+calculate_cost([_],0).
+calculate_cost([Head|[Second|Rest]],Cost):-
+    get_cost(Head, Second, C1),
+    calculate_cost([Second|Rest], CRest),
+    Cost is C1 + CRest.
+
+%Depth-first Search Implementation
+depth_first_search(Start, Goal,Path):-
+    dfs(Start, Goal, [Start], Path).
+
+% dfs/4 Goal node found.
+dfs(Goal, Goal,_,[Goal]).
+
+% dfs/4 Recurvice depth search.
+dfs(Start, Goal, Visited, [Start|Path]):-
+    neighbor(Start, Node),
+    not(member(Node,Visited)),
+    dfs(Node, Goal, [Node|Visited], Path).
+
+
+%Breadth-first Search Implementation
+breadth_first_search(Start, Goal, Path) :-
+    bfs([0-(Start, [Start])], Goal, RevPath),
+    reverse(RevPath, Path).
+
+% bfs/3 Goal node found.
+bfs([_C-(Node, Path) | _], Node, Path).
+
+% bfs/3Recursive case: Continue BFS
+bfs([_C-(_Node, Path) | Rest], Goal, FinalPath) :-
+    expand(Path, NewNodes),
+    append(Rest, NewNodes, NewQueue),
+    keysort(NewQueue, SortedQueue),
+    bfs(SortedQueue, Goal, FinalPath).
+
+% expand/2 predicate to generate neighboring nodes
+expand([Node|Path], Neighbors) :-
+    findall(C-(Neighbor,[Neighbor | [Node|Path]]),
+            (neighbor(Node, Neighbor), not(member(Neighbor, Path)), calculate_cost([Neighbor | [Node|Path]], C)),
+            Neighbors).
+
+
+% A* Search Implementation
+a_star_search(Start, Goal, Path) :-
+    heuristic(Start, Goal, F),
+    a_star([F-(0, Start, [Start])], Goal, RevPath),
+    reverse(RevPath, Path).
+
+% Base case: Goal node found
+a_star([_F-(_G, Node, Path) | _], Node, Path).
+
+% Recursive case: Continue A* search
+a_star([_F-(G, _Node, Path) | Rest], Goal, FinalPath) :-
+    expand(Path, NewNodes, G, Goal),
+    append(Rest, NewNodes, NewQueue),
+    keysort(NewQueue, SortedQueue),
+    a_star(SortedQueue, Goal, FinalPath).
+
+% expand/5 predicate to generate neighboring nodes with cost
+expand([Node|Path], Neighbors, G_prev, Goal) :-
+    findall(F-(G, Neighbor, [Neighbor | [Node|Path]]),
+            (neighbor(Node, Neighbor), not(member(Neighbor, Path)),
+             calculate_cost([Neighbor|[Node|Path]], Cost),
+             heuristic(Neighbor, Goal, H),
+             G is G_prev + Cost, F is G + H),
+            Neighbors).
+
+% Heuristic function to calculate the estimated distance between nodes using latitude and longitude.
+heuristic(Node, Destination, Heuristic) :-
+    location(Node, Lat1, Lon1),
+    location(Destination, Lat2, Lon2),
+    haversine_distance(Lat1, Lon1, Lat2, Lon2, Heuristic).
+
+% Convert degrees to radians
+degrees_to_radians(Degrees, Radians) :-
+    Radians is Degrees * (pi / 180).
+
+% Haversine formula to calculate the distance between two points on Earth
+haversine_distance(Lat1, Lon1, Lat2, Lon2, Distance) :-
+    degrees_to_radians(Lat1, Lat1Rad),
+    degrees_to_radians(Lon1, Lon1Rad),
+    degrees_to_radians(Lat2, Lat2Rad),
+    degrees_to_radians(Lon2, Lon2Rad),
+    DLat is Lat2Rad - Lat1Rad,
+    DLon is Lon2Rad - Lon1Rad,
+    A is sin(DLat / 2) ** 2 + cos(Lat1Rad) * cos(Lat2Rad) * sin(DLon / 2) ** 2,
+    C is 2 * atan2(sqrt(A), sqrt(1 - A)),
+    Radius is 6371,  % Earth's radius in kilometers
+    Distance is Radius * C.

src/utils.py

@@ -0,0 +1,126 @@
+import os
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+class Utils:
+    
+    def __init__(self, dataset_dir = "dataset/"):    
+        self.london_stations_df = pd.read_csv(os.path.join(dataset_dir,"stations.csv"))
+        self.london_connections_df = pd.read_csv(os.path.join(dataset_dir,"connections.csv"))
+        self.london_lines_df = pd.read_csv(os.path.join(dataset_dir,"lines.csv"))
+
+
+    def get_all_station_names(self):
+        return self.london_stations_df['name'].tolist()
+
+    def remove_spl_chars(self, text):
+        return text.lower().replace(" ", "_").replace("'", "").replace("(", "_").replace(")", "_").replace("&", "and").replace(".", "").replace(",", "")
+
+    def parse_route_by_prolog_name(self, prolog_name):
+        return self.london_stations_df[self.london_stations_df['prolog_name'] == prolog_name]
+
+    def get_line_color_for_prolog_name(self, name1, name2):
+        station1 = self.parse_route_by_prolog_name(name1)
+        station2 = self.parse_route_by_prolog_name(name2)
+        connection = self.london_connections_df[(self.london_connections_df['station1'] == station1['id'].values[0]) & (
+            self.london_connections_df['station2'] == station2['id'].values[0])]
+        if len(connection) == 0:
+            connection = self.london_connections_df[(self.london_connections_df['station1'] == station2['id'].values[0]) & (
+                self.london_connections_df['station2'] == station1['id'].values[0])]
+        line = self.london_lines_df[self.london_lines_df['line']
+                                    == connection['line'].values[0]]
+        color = "#"+line['colour'].values[0]
+        return color
+    
+    def get_line_name_for_prolog_name(self, name1, name2):
+        station1 = self.parse_route_by_prolog_name(name1)
+        station2 = self.parse_route_by_prolog_name(name2)        
+        connection = self.london_connections_df[(self.london_connections_df['station1'] == station1['id'].values[0]) & (
+            self.london_connections_df['station2'] == station2['id'].values[0])]
+        if len(connection) == 0:
+            connection = self.london_connections_df[(self.london_connections_df['station1'] == station2['id'].values[0]) & (
+                self.london_connections_df['station2'] == station1['id'].values[0])]
+        line = self.london_lines_df[self.london_lines_df['line']
+                                    == connection['line'].values[0]]
+        return line['name'].values[0]
+
+    def get_traces(self):
+        lines = []
+        for station1, station2, line, time in self.london_connections_df.values.tolist():
+            station1_lat = self.london_stations_df[self.london_stations_df['id']
+                                                   == station1]['latitude'].values[0]
+            station1_long = self.london_stations_df[self.london_stations_df['id']
+                                                    == station1]['longitude'].values[0]
+            station2_lat = self.london_stations_df[self.london_stations_df['id']
+                                                   == station2]['latitude'].values[0]
+            station2_long = self.london_stations_df[self.london_stations_df['id']
+                                                    == station2]['longitude'].values[0]
+            name = self.london_lines_df[self.london_lines_df['line']
+                                        == line]['name'].values[0]
+            color = self.london_lines_df[self.london_lines_df['line']
+                                         == line]['colour'].values[0]
+            lines.append(go.Scattermapbox(
+                mode="lines",
+                lat=[station1_lat, station2_lat],
+                lon=[station1_long, station2_long],
+                line={'width': 4, 'color': "#"+color},
+                name=name))
+        return lines
+    
+    def filter_duplicate_traces(self, fig):
+        names = set()
+        fig.for_each_trace(lambda trace: trace.update(showlegend=False) if (trace.name in names) else names.add(trace.name))
+        return fig
+
+    def get_map_center(self):
+        return self.london_stations_df['latitude'].mean(), self.london_stations_df['longitude'].mean()
+
+    def get_default_map_layout(self, center_lat, center_lon):
+        return go.Layout(
+            mapbox_style="carto-positron",
+            mapbox=dict(
+                bearing=0,
+                pitch=0,
+                center=dict(lat=center_lat, lon=center_lon),
+                zoom=11.8
+            ))
+
+    def get_markers(self):
+        return go.Scattermapbox(
+            lat=self.london_stations_df['latitude'].tolist(),
+            lon=self.london_stations_df['longitude'].tolist(),
+            text=self.london_stations_df['name'].tolist(),
+            name="Stations",
+            mode='markers+text',
+            textposition="bottom center",
+            marker=go.scattermapbox.Marker(size=10)
+        )
+
+    def filter_marker_and_traces(self, route):
+        route_station = [self.parse_route_by_prolog_name(x) for x in route]
+        lats = [x['latitude'].values[0] for x in route_station]
+        longs = [x['longitude'].values[0] for x in route_station]
+        names = [x['name'].values[0] for x in route_station]
+        traces = [go.Scattermapbox(
+            text=[names[i], names[i+1]],
+            mode="markers+text+lines",
+            lat=[lats[i], lats[i+1]],
+            lon=[longs[i], longs[i+1]],
+            line={'width': 4, 'color': self.get_line_color_for_prolog_name(
+                route[i], route[i+1])},
+            name=self.get_line_name_for_prolog_name(route[i], route[i+1]),
+            marker=go.scattermapbox.Marker(size=10)
+        ) for i in range(len(route_station)-1)]
+        traces.append(go.Scattermapbox(
+            mode="markers",
+            lat=[lats[0], lats[-1]],
+            lon=[longs[0], longs[-1]],
+            marker=go.scattermapbox.Marker(
+                size=[10,15], color=["blue", "red"]),
+            name="Stations"
+        ))
+        center_lat = np.array(lats).mean()
+        center_lon = np.array(longs).mean()
+        output_text = " -> ".join(names)
+        return traces, center_lat, center_lon, output_text