Merge branch 'IT19186566' into 'master'

Algorithm completed

See merge request !7

routeplanner/AL.py

+from helpers import load_map_test, load_map_bus, load_map_train,route_177
+import math
+
+val = 0
+
+class PathPlanner():
+    
+    def __init__(self, M, start=None, goal=None, type=None):
+        
+        self.map = M
+        self.start= start
+        self.goal = goal
+        self.type = type
+        self.dis = 0
+
+        self.closedSet = self.create_closedSet() if goal != None and start != None else None
+        self.openSet = self.create_openSet() if goal != None and start != None else None
+        self.cameFrom = self.create_cameFrom() if goal != None and start != None else None
+        self.gScore = self.create_gScore() if goal != None and start != None else None
+        self.fScore = self.create_fScore() if goal != None and start != None else None
+        self.path = self.run_search() if self.map and self.start != None and self.goal != None else None
+    
+    def reconstruct_path(self, current):
+        total_path = [current]
+        while current in self.cameFrom.keys():
+            current = self.cameFrom[current]
+            total_path.append(current)
+        return total_path
+    
+    def _reset(self):
+        self.closedSet = None
+        self.openSet = None
+        self.cameFrom = None
+        self.gScore = None
+        self.fScore = None
+        self.path = self.run_search() if self.map and self.start and self.goal else None
+
+    def run_search(self):
+        """ """
+        if self.map == None:
+            raise(ValueError, "Must create map before running search")
+        if self.goal == None:
+            raise(ValueError, "Must create goal node before running search")
+        if self.start == None:
+            raise(ValueError, "Must create start node before running search")
+
+        self.closedSet = self.closedSet if self.closedSet != None else self.create_closedSet()
+        self.openSet = self.openSet if self.openSet != None else  self.create_openSet()
+        self.cameFrom = self.cameFrom if self.cameFrom != None else  self.create_cameFrom()
+        self.gScore = self.gScore if self.gScore != None else  self.create_gScore()
+        self.fScore = self.fScore if self.fScore != None else  self.create_fScore()
+
+        while not self.is_open_empty():
+            current = self.get_current_node()
+
+            if current == self.goal:
+                self.path = [x for x in reversed(self.reconstruct_path(current))]
+                return self.path
+            else:
+                self.openSet.remove(current)
+                self.closedSet.add(current)
+
+            for neighbor in self.get_neighbors(current):
+                if neighbor in self.closedSet:
+                    continue    # Ignore the neighbor which is already evaluated.
+
+                if not neighbor in self.openSet:    # Discover a new node
+                    self.openSet.add(neighbor)
+                
+                # The distance from start to a neighbor
+                if self.getTempGScore(current, neighbor) >= self.get_gScore(neighbor):
+                    continue 
+
+
+                # This path is the best until now. Record it!
+                global val  #
+                val = val + self.dis
+                self.record_best_path_to(current, neighbor)
+        print("No Path Found")
+        self.path = None
+        return False
+
+    def create_closedSet(self):
+        return set()
+
+    def create_openSet(self):
+
+        openSet = set()
+        openSet.add(self.start)
+        return openSet
+        
+    def create_cameFrom(self):
+        cameFrom = {}
+        return cameFrom
+
+    def create_gScore(self):
+        gScore = {}
+        for node in self.map.intersections.keys():
+            if node == self.start:
+                gScore[node] = 0
+            else: gScore[node] = float('inf')
+        return gScore
+
+    def create_fScore(self):
+        if self.start != None:
+            fScore = {}
+            for node in self.map.intersections.keys():
+                if node == self.start:
+                    fScore[node] = self.heuristic_cost_estimate(self.start)
+                else: fScore[node] = float('inf')
+            return fScore
+        raise(ValueError, "Must create start node before creating fScore.")
+
+    def set_map(self, M):
+        "set map attribute "
+        self._reset(self)
+        self.start = None
+        self.goal = None 
+        self.map = M
+
+    def set_start(self, start):
+        " set start attribute "
+        self._reset(self)
+        self.start = start
+        self.goal = None
+        
+    def set_goal(self, goal):
+        "set goal attribute "
+        self._reset(self)
+        self.goal = goal
+
+    #-------------------------------- get information --------------------------------#
+
+    def is_open_empty(self):
+        "returns True if the open set is empty"
+        return not bool(self.openSet)
+    def get_current_node(self):
+        " Returns the node in the open set with the lowest value of f(node)"
+        
+        current = None
+        minim = float('inf')
+        for node in self.openSet:
+            if self.fScore[node] < minim:
+                minim = self.fScore[node]
+                current = node
+        return current        
+
+    def get_neighbors(self, node):
+        """Returns the neighbors of a node"""
+        return set(self.map.roads[node]) 
+
+    def get_traveled_distance(self):
+        #traveled_distance 
+        return self.dis
+
+    #-------------------------------- Calculations --------------------------------#
+
+    def get_gScore(self, node):
+        return self.gScore[node]
+
+    def getTempGScore(self, current, neighbor):
+        # distance from the current node to it's neighbors
+        
+        g_score_current = self.get_gScore(current)
+        dist_current_neighbor = self.distance(current,neighbor)
+        return g_score_current+dist_current_neighbor
+
+    def heuristic_cost_estimate(self, node):
+
+        if self.goal != None:
+            heuristic_estimate = self.distance(node,self.goal)
+            return heuristic_estimate
+        raise(ValueError, "Must create goal node before calculating huristic ")
+
+    def calculate_fscore(self, node):
+        #  F = G + H
+        
+        f_score = self.get_gScore(node) + self.heuristic_cost_estimate(node)
+        return f_score
+
+
+    # best path
+    def record_best_path_to(self, current, neighbor):
+        
+        self.cameFrom[neighbor] = current
+        self.gScore[neighbor] = self.getTempGScore(current,neighbor)
+        self.fScore[neighbor] = self.gScore[neighbor] + self.heuristic_cost_estimate(neighbor)
+
+    def distance(self, node_1, node_2):
+        from math import radians, cos, sin, asin, sqrt
+
+        #The math module contains a function named
+        #radians which converts from degrees to radians.
+
+        lon1 = radians(self.map.intersections [node_1][0])
+        lon2 = radians(self.map.intersections [node_2][0])
+        lat1 = radians(self.map.intersections [node_1][1])
+        lat2 = radians(self.map.intersections [node_2][1])
+        
+        # Haversine formula
+        dlon = lon2 - lon1
+        dlat = lat2 - lat1
+        a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
+    
+        c = 2 * asin(sqrt(a))
+        
+        # Radius of earth in kilometers. Use 3956 for miles
+        r = 6371
+        
+        # calculate the result
+        dist = (c * r)
+        #print (dist)
+        self.dis = dist
+        return dist
+
+
+# Get route  
+def main( a, node1, node2):
+    if a == 3:
+        map = load_map_train()
+    elif a == 2:
+        map = load_map_bus()
+    else:
+        map = load_map_test()
+    
+    planner = PathPlanner(map,node1,node2)
+    print(map.roads)
+
+    path = planner.path
+    print('distance =' ,val)
+    if path == [5, 16, 37, 12, 34]:
+        print("code works for test path!!!")
+        print(path)
+    else:
+        print("Genarated path")
+        
+        print(path)
+        
+        # for x in path:
+        #     if routeNo
+        #     print (x)
+        
+        #for each in path:
+            
+
+
+main( 1, 2, 14)   
\ No newline at end of file

routeplanner/helpers.py

+
+import networkx as nx
+import pickle
+from chart_studio import plotly as py
+import random
+from plotly.graph_objs import *
+from plotly.offline import init_notebook_mode, plot, iplot
+#init_notebook_mode(connected=True)
+
+
+route_177 = { 
+	1: 34, 
+	2: 42, 
+	3: 54, 
+	4: 67, 
+	5: 80, 
+	6: 92, 
+	7: 105, 
+	8: 109, 
+	9: 117,
+	10: 126,
+	11: 134,
+	12: 140,
+	13: 149,
+	14: 157,
+	15: 163,
+}
+
+map_test = {
+	0: {'pos': (6.911034627182109, 79.84918916006576), 'connections': [1], 'name' : 'Kollupitiya','type': 0,'routeNo': 177, 'hValue': 1}, 
+	1: {'pos': (6.911751932322411, 79.86194701315071), 'connections': [0,2], 'name' : 'Viharamahadevi Park', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	2: {'pos': (6.911385550864001, 79.87682791026592), 'connections': [1,3], 'name' : 'House Of Fashion', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	3: {'pos': (6.911031363415147, 79.88498429384545), 'connections': [2,4], 'name' : 'Castle Street', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	4: {'pos': (6.908462881966912, 79.89338919261249), 'connections': [3,5], 'name' : 'Rajagiriya', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	5: {'pos': (6.906663916178293, 79.90205413217801), 'connections': [4,6], 'name' : 'Hsbc Rajagiriya', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	6: {'pos': (6.90333333857459, 79.90747047529919), 'connections': [5,7], 'name' : 'Ethulkotte New', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	7: {'pos': (6.903185701293392, 79.91168232658337), 'connections': [6,8], 'name' : 'Parliament Junction', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	8: {'pos': (6.902102452411621, 79.91741047710077), 'connections': [7,9], 'name' : 'Battaramulla Junction', 'type': 0, 'routeNo': 177, 'hValue': 1}, 
+	9: {'pos': (6.90431022366131, 79.92400480782847), 'connections': [8,10], 'name' : 'Ganahena', 'type': 0, 'routeNo': 177, 'hValue': 1},
+	10: {'pos': (6.906173952520986, 79.92862087153598), 'connections': [9,11], 'name' : 'Koswatta', 'type': 0, 'routeNo': 177, 'hValue': 1},
+	11: {'pos': (6.9084657304543455, 79.9383708894408), 'connections': [10,12], 'name' : 'Kotte-Bope', 'type': 0, 'routeNo': 177, 'hValue': 1},
+	12: {'pos': (6.9085023402918155, 79.94425286198016), 'connections': [11,13], 'name' : 'Thalahena Junction', 'type': 0, 'routeNo': 177, 'hValue': 1},
+	13: {'pos': (6.903962700873259, 79.95430199947661), 'connections': [12,14], 'name' : 'Malabe', 'type': 0, 'routeNo': 177, 'hValue': 1},
+	14: {'pos': (6.933539686667202, 79.85005389798111), 'connections': [15], 'name' : 'Fort', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+	15: {'pos': (6.923619774894732, 79.84965509086771), 'connections': [14,16], 'name' : 'Kompannavidiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+	16: {'pos': (6.911282166657041, 79.84821747831087), 'connections': [15,1,17], 'name' : 'Kollupitiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+	17: {'pos': (6.893710110662125, 79.85300590028642), 'connections': [16,18], 'name' : 'Bambalapitiya', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+	18: {'pos': (6.875030063532378, 79.85744793142631), 'connections': [17,19], 'name' : 'Wellawatte', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+	19: {'pos': (6.850915755796403, 79.8620969312958), 'connections': [18], 'name' : 'Dehiwala ', 'type': 1, 'routeNo': 8717, 'hValue': 1},
+}
+
+
+class Map:
+	def __init__(self, G):
+		self._graph = G
+		self.intersections = nx.get_node_attributes(G, "pos")
+		self.routeNo = nx.get_node_attributes(G, "routeNo")
+		self.type = nx.get_node_attributes(G, "type")
+		self.roads = [list(G[node]) for node in G.nodes()]
+		
+		
+
+	def save(self, filename):
+		with open(filename, 'wb') as f:
+			pickle.dump(self._graph, f)
+
+def load_map_graph(map_dict):
+	G = nx.Graph()
+	for node in map_dict.keys():
+		G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
+	for node in map_dict.keys():
+		for con_node in map_dict[node]['connections']:
+			G.add_edge(node, con_node)
+	
+	return G
+	
+
+def load_map_b(map_dict):
+	# G = nx.Graph()
+	# for node in map_dict.keys():
+	# 	#nodeValue = 
+	# 	if map_dict[node]['type'] != 1:
+	# 		continue
+	# 	else:
+	# 		G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])
+	G = nx.Graph()
+	for node in map_dict.keys():
+		G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])	
+	# for node in map_dict.keys():
+	# 	for con_node in map_dict[node]['connections']:
+	# 		if map_dict[con_node]['type'] != 1:
+	# 			continue
+	# 		else:
+	# 			G.add_edge(node, con_node)
+	for node in map_dict.keys():
+		for con_node in map_dict[node]['connections']:
+			if map_dict[node]['type'] != 1:
+				continue
+			elif map_dict[con_node]['type'] != 1:
+				continue
+			else:
+				G.add_edge(node, con_node)
+	return G
+
+#map for train
+def load_map_t(map_dict):
+	G = nx.Graph()
+	for node in map_dict.keys():
+		G.add_node(node, pos=map_dict[node]['pos'], type=map_dict[node]['type'],routeNo=map_dict[node]['routeNo'])	
+	
+	for node in map_dict.keys():
+		for con_node in map_dict[node]['connections']:
+			if map_dict[node]['type'] == 1:
+				continue
+			elif map_dict[con_node]['type'] == 1:
+				continue
+			else:
+				G.add_edge(node, con_node)
+	return G
+
+def load_map_test():
+	G = load_map_graph(map_test)
+	return Map(G)
+
+def load_map_bus():
+	G = load_map_b(map_test)
+	return Map(G)
+
+def load_map_train():
+	G = load_map_t(map_test)
+	return Map(G)
+

routeplanner/maps.py

-
-import networkx as nx
-import pickle
-from chart_studio import plotly as py
-#import plotly.plotly as py
-import random
-from plotly.graph_objs import *
-from plotly.offline import init_notebook_mode, plot, iplot
-init_notebook_mode(connected=True)
-
-
-map_10_dict = {
-	0: {'pos': (0.7798606835438107, 0.6922727646627362), 'connections': [7, 6, 5]}, 
-	1: {'pos': (0.7647837074641568, 0.3252670836724646), 'connections': [4, 3, 2]}, 
-	2: {'pos': (0.7155217893995438, 0.20026498027300055), 'connections': [4, 3, 1]}, 
-	3: {'pos': (0.7076566826610747, 0.3278339270610988), 'connections': [5, 4, 1, 2]}, 
-	4: {'pos': (0.8325506249953353, 0.02310946309985762), 'connections': [1, 2, 3]}, 
-	5: {'pos': (0.49016747075266875, 0.5464878695400415), 'connections': [7, 0, 3]}, 
-	6: {'pos': (0.8820353070895344, 0.6791919587749445), 'connections': [0]}, 
-	7: {'pos': (0.46247219371675075, 0.6258061621642713), 'connections': [0, 5]}, 
-	8: {'pos': (0.11622158839385677, 0.11236327488812581), 'connections': [9]}, 
-	9: {'pos': (0.1285377678230034, 0.3285840695698353), 'connections': [8]}
-}
-
-
-class Map:
-	def __init__(self, G):
-		self._graph = G
-		self.intersections = nx.get_node_attributes(G, "pos")
-		self.roads = [list(G[node]) for node in G.nodes()]
-
-	def save(self, filename):
-		with open(filename, 'wb') as f:
-			pickle.dump(self._graph, f)
-
-def load_map_graph(map_dict):
-	G = nx.Graph()
-	for node in map_dict.keys():
-		G.add_node(node, pos=map_dict[node]['pos'])
-	for node in map_dict.keys():
-		for con_node in map_dict[node]['connections']:
-			G.add_edge(node, con_node)
-	return G
-
-def load_map_10():
-	G = load_map_graph(map_10_dict)
-	return Map(G)
-
-def load_map_40():
-	G = load_map_graph(map_40_dict)
-	return Map(G)
-
-def show_map(M, start=None, goal=None, path=None):
-    G = M._graph
-    pos = nx.get_node_attributes(G, 'pos')
-    edge_trace = Scatter(
-    x=[],
-    y=[],
-    line=Line(width=0.5,color='#888'),
-    hoverinfo='none',
-    mode='lines')
-
-    for edge in G.edges():
-        x0, y0 = G.node[edge[0]]['pos']
-        x1, y1 = G.node[edge[1]]['pos']
-        edge_trace['x'] += [x0, x1, None]
-        edge_trace['y'] += [y0, y1, None]
-
-    node_trace = Scatter(
-        x=[],
-        y=[],
-        text=[],
-        mode='markers',
-        hoverinfo='text',
-        marker=Marker(
-            showscale=False,
-            # colorscale options
-            # 'Greys' | 'Greens' | 'Bluered' | 'Hot' | 'Picnic' | 'Portland' |
-            # Jet' | 'RdBu' | 'Blackbody' | 'Earth' | 'Electric' | 'YIOrRd' | 'YIGnBu'
-            colorscale='Hot',
-            reversescale=True,
-            color=[],
-            size=10,
-            colorbar=dict(
-                thickness=15,
-                title='Node Connections',
-                xanchor='left',
-                titleside='right'
-            ),
-            line=dict(width=2)))
-    for node in G.nodes():
-        x, y = G.node[node]['pos']
-        node_trace['x'].append(x)
-        node_trace['y'].append(y)
-
-    for node, adjacencies in enumerate(G.adjacency_list()):
-        color = 0
-        if path and node in path:
-            color = 2
-        if node == start:
-            color = 3
-        elif node == goal:
-            color = 1
-        # node_trace['marker']['color'].append(len(adjacencies))
-        node_trace['marker']['color'].append(color)
-        node_info = "Intersection " + str(node)
-        node_trace['text'].append(node_info)
-
-    fig = Figure(data=Data([edge_trace, node_trace]),
-                 layout=Layout(
-                    title='<br>Network graph made with Python',
-                    titlefont=dict(size=16),
-                    showlegend=False,
-                    hovermode='closest',
-                    margin=dict(b=20,l=5,r=5,t=40),
-                   
-                    xaxis=XAxis(showgrid=False, zeroline=False, showticklabels=False),
-                    yaxis=YAxis(showgrid=False, zeroline=False, showticklabels=False)))
-
-    iplot(fig)

routeplanner/project_notebook.ipynb

-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from maps import Map, load_map_10, show_map\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_10 = load_map_10()\n",
-    "show_map(map_10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_10.intersections\n",
-    "#map_10.intersections[0][0]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# this shows that intersection 0 connects to intersections 7, 6, and 5\n",
-    "map_10.roads[0] "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This shows the full connectivity of the map\n",
-    "map_10.roads"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters in the function\n",
-    "show_map(map_10, start=7, goal=2, path=[7,5,5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Planner\n",
-    "class PathPlanner():\n",
-    "    \"\"\" PathPlanner Object \"\"\"\n",
-    "    def __init__(self, M, start=None, goal=None):\n",
-    "        \n",
-    "        self.map = M\n",
-    "        self.start= start\n",
-    "        self.goal = goal\n",
-    "        self.closedSet = self.create_closedSet() if goal != None and start != None else None\n",
-    "        self.openSet = self.create_openSet() if goal != None and start != None else None\n",
-    "        self.cameFrom = self.create_cameFrom() if goal != None and start != None else None\n",
-    "        self.gScore = self.create_gScore() if goal != None and start != None else None\n",
-    "        self.fScore = self.create_fScore() if goal != None and start != None else None\n",
-    "        self.path = self.run_search() if self.map and self.start != None and self.goal != None else None\n",
-    "    \n",
-    "    def reconstruct_path(self, current):\n",
-    "        total_path = [current]\n",
-    "        while current in self.cameFrom.keys():\n",
-    "            current = self.cameFrom[current]\n",
-    "            total_path.append(current)\n",
-    "        return total_path\n",
-    "    \n",
-    "    def _reset(self):\n",
-    "        self.closedSet = None\n",
-    "        self.openSet = None\n",
-    "        self.cameFrom = None\n",
-    "        self.gScore = None\n",
-    "        self.fScore = None\n",
-    "        self.path = self.run_search() if self.map and self.start and self.goal else None\n",
-    "\n",
-    "    def run_search(self):\n",
-    "        \"\"\" \"\"\"\n",
-    "        if self.map == None:\n",
-    "            raise(ValueError, \"Must create map before running search\")\n",
-    "        if self.goal == None:\n",
-    "            raise(ValueError, \"Must create goal node before running search\")\n",
-    "        if self.start == None:\n",
-    "            raise(ValueError, \"Must create start node before running search\")\n",
-    "\n",
-    "        self.closedSet = self.closedSet if self.closedSet != None else self.create_closedSet()\n",
-    "        self.openSet = self.openSet if self.openSet != None else  self.create_openSet()\n",
-    "        self.cameFrom = self.cameFrom if self.cameFrom != None else  self.create_cameFrom()\n",
-    "        self.gScore = self.gScore if self.gScore != None else  self.create_gScore()\n",
-    "        self.fScore = self.fScore if self.fScore != None else  self.create_fScore()\n",
-    "\n",
-    "        while not self.is_open_empty():\n",
-    "            current = self.get_current_node()\n",
-    "\n",
-    "            if current == self.goal:\n",
-    "                self.path = [x for x in reversed(self.reconstruct_path(current))]\n",
-    "                return self.path\n",
-    "            else:\n",
-    "                self.openSet.remove(current)\n",
-    "                self.closedSet.add(current)\n",
-    "\n",
-    "            for neighbor in self.get_neighbors(current):\n",
-    "                if neighbor in self.closedSet:\n",
-    "                    continue    # Ignore the neighbor which is already evaluated.\n",
-    "\n",
-    "                if not neighbor in self.openSet:    # Discover a new node\n",
-    "                    self.openSet.add(neighbor)\n",
-    "                \n",
-    "                # The distance from start to a neighbor\n",
-    "                #the \"dist_between\" function may vary as per the solution requirements.\n",
-    "                if self.get_tentative_gScore(current, neighbor) >= self.get_gScore(neighbor):\n",
-    "                    continue    \n",
-    "                \n",
-    "                self.record_best_path_to(current, neighbor)\n",
-    "        print(\"No Path Found\")\n",
-    "        self.path = None\n",
-    "        return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_closedSet(self):\n",
-    "    \"\"\" Creates and returns a data structure suitable to hold the set of nodes already evaluated\"\"\"\n",
-    "    # EXAMPLE: return a data structure suitable to hold the set of nodes already evaluated\n",
-    "    return set()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_openSet(self):\n",
-    "    if self.start != None:\n",
-    "        # TODO: return a data structure suitable to hold the set of currently discovered nodes \n",
-    "        # that are not evaluated yet. Make sure to include the start node.\n",
-    "        openSet = set()\n",
-    "        openSet.add(self.start)\n",
-    "        return openSet\n",
-    "    \n",
-    "    raise(ValueError, \"Must create start node before creating an open set. Try running PathPlanner.set_start(start_node)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_cameFrom(self):\n",
-    "    # TODO: return a data structure that shows which node can most efficiently be reached from another,\n",
-    "    # for each node. \n",
-    "    cameFrom = {}\n",
-    "    return cameFrom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_gScore(self):\n",
-    "    # TODO:  return a data structure that holds the cost of getting from the start node to that node, for each node.\n",
-    "    # for each node. The cost of going from start to start is zero. The rest of the node's values should \n",
-    "    # be set to infinity.\n",
-    "    if self.start != None:\n",
-    "        gScore = {}\n",
-    "        for node in self.map.intersections.keys():\n",
-    "            if node == self.start:\n",
-    "                gScore[node] = 0\n",
-    "            else: gScore[node] = float('inf')\n",
-    "        return gScore\n",
-    "    raise(ValueError, \"Must create start node before creating gScore. Try running PathPlanner.set_start(start_node)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_fScore(self):\n",
-    "    # TODO: return a data structure that holds the total cost of getting from the start node to the goal\n",
-    "    # by passing by that node, for each node. That value is partly known, partly heuristic.\n",
-    "    # For the first node, that value is completely heuristic. The rest of the node's value should be \n",
-    "    # set to infinity.\n",
-    "    if self.start != None:\n",
-    "        fScore = {}\n",
-    "        for node in self.map.intersections.keys():\n",
-    "            if node == self.start:\n",
-    "                fScore[node] = self.heuristic_cost_estimate(self.start)\n",
-    "            else: fScore[node] = float('inf')\n",
-    "        return fScore\n",
-    "    raise(ValueError, \"Must create start node before creating fScore. Try running PathPlanner.set_start(start_node)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def set_map(self, M):\n",
-    "    \"\"\"Method used to set map attribute \"\"\"\n",
-    "    self._reset(self)\n",
-    "    self.start = None\n",
-    "    self.goal = None\n",
-    "    # TODO: Set map to new value. \n",
-    "    self.map = M"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def set_start(self, start):\n",
-    "    \"\"\"Method used to set start attribute \"\"\"\n",
-    "    self._reset(self)\n",
-    "    # TODO: Set start value. Remember to remove goal, closedSet, openSet, cameFrom, gScore, fScore, \n",
-    "    # and path attributes' values.\n",
-    "    self.start = start\n",
-    "    self.goal = None\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def set_goal(self, goal):\n",
-    "    \"\"\"Method used to set goal attribute \"\"\"\n",
-    "    self._reset(self)\n",
-    "    # TODO: Set goal value. \n",
-    "    self.goal = goal\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def is_open_empty(self):\n",
-    "    \"\"\"returns True if the open set is empty. False otherwise. \"\"\"\n",
-    "    # TODO: Return True if the open set is empty. False otherwise.\n",
-    "    return not bool(self.openSet)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_current_node(self):\n",
-    "    \"\"\" Returns the node in the open set with the lowest value of f(node).\"\"\"\n",
-    "    # TODO: Return the node in the open set with the lowest value of f(node).\n",
-    "    current = None\n",
-    "    minim = float('inf')\n",
-    "    for node in self.openSet:\n",
-    "        if self.fScore[node] < minim:\n",
-    "            minim = self.fScore[node]\n",
-    "            current = node\n",
-    "    return current        \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_neighbors(self, node):\n",
-    "    \"\"\"Returns the neighbors of a node\"\"\"\n",
-    "    # TODO: Return the neighbors of a node\n",
-    "    return set(self.map.roads[node]) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scores\n",
-    "\n",
-    "def get_gScore(self, node):\n",
-    "    \"\"\"Returns the g Score of a node\"\"\"\n",
-    "    # TODO: Return the g Score of a node\n",
-    "    return self.gScore[node]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def distance(self, node_1, node_2):\n",
-    "    \"\"\" Computes the Euclidean L2 Distance\"\"\"\n",
-    "    # TODO: Compute and return the Euclidean L2 Distance\n",
-    "    x1 = self.map.intersections[node_1][0]\n",
-    "    y1 = self.map.intersections[node_1][1]\n",
-    "    x2 = self.map.intersections[node_2][0]\n",
-    "    y2 = self.map.intersections[node_2][1]\n",
-    "    dist = math.sqrt( (x2-x1)**2 + (y2-y1)**2 )\n",
-    "    return dist\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_tentative_gScore(self, current, neighbor):\n",
-    "    \"\"\"Returns the tentative g Score of a node\"\"\"\n",
-    "    # TODO: Return the g Score of the current node \n",
-    "    # plus distance from the current node to it's neighbors\n",
-    "    g_score_current = self.get_gScore(current)\n",
-    "    dist_current_neighbor = self.distance(current,neighbor)\n",
-    "    return g_score_current+dist_current_neighbor\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def heuristic_cost_estimate(self, node):\n",
-    "    \"\"\" Returns the heuristic cost estimate of a node \"\"\"\n",
-    "    # TODO: Return the heuristic cost estimate of a node\n",
-    "    if self.goal != None:\n",
-    "        heuristic_estimate = self.distance(node,self.goal)\n",
-    "        return heuristic_estimate\n",
-    "    raise(ValueError, \"Must create goal node before calculating huristic estimate. Try running PathPlanner.set_goal(goal_node)\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calculate_fscore(self, node):\n",
-    "    \"\"\"Calculate the f score of a node. \"\"\"\n",
-    "    # TODO: Calculate and returns the f score of a node. \n",
-    "    # REMEMBER F = G + H\n",
-    "    f_score = self.get_gScore(node) + self.heuristic_cost_estimate(node)\n",
-    "    return f_score\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def record_best_path_to(self, current, neighbor):\n",
-    "    \"\"\"Record the best path to a node \"\"\"\n",
-    "    # TODO: Record the best path to a node, by updating cameFrom, gScore, and fScore\n",
-    "    self.cameFrom[neighbor] = current\n",
-    "    self.gScore[neighbor] = self.get_tentative_gScore(current,neighbor)\n",
-    "    self.fScore[neighbor] = self.gScore[neighbor] + self.heuristic_cost_estimate(neighbor)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Associates implemented functions with PathPlanner class\n",
-    "PathPlanner.create_closedSet = create_closedSet\n",
-    "PathPlanner.create_openSet = create_openSet\n",
-    "PathPlanner.create_cameFrom = create_cameFrom\n",
-    "PathPlanner.create_gScore = create_gScore\n",
-    "PathPlanner.create_fScore = create_fScore\n",
-    "PathPlanner.set_map = set_map\n",
-    "PathPlanner.set_start = set_start\n",
-    "PathPlanner.set_goal = set_goal\n",
-    "PathPlanner.is_open_empty = is_open_empty\n",
-    "PathPlanner.get_current_node = get_current_node\n",
-    "PathPlanner.get_neighbors = get_neighbors\n",
-    "PathPlanner.get_gScore = get_gScore\n",
-    "PathPlanner.distance = distance\n",
-    "PathPlanner.get_tentative_gScore = get_tentative_gScore\n",
-    "PathPlanner.heuristic_cost_estimate = heuristic_cost_estimate\n",
-    "PathPlanner.calculate_fscore = calculate_fscore\n",
-    "PathPlanner.record_best_path_to = record_best_path_to"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Visualize result\n",
-    "start = 7\n",
-    "goal = 5\n",
-    "\n",
-    "show_map(map_10, start=start, goal=goal, path=PathPlanner(map_10, start, goal).path)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

routeplanner/test.py

+from helpers import load_map_40
+
+MAP_40_ANSWERS = [
+    (5, 34, [5, 16, 37, 12, 34]),
+    (5, 5,  [5]),
+    (8, 24, [8, 14, 16, 37, 12, 17, 10, 24])
+]
+
+def test(shortest_path_function):
+    map_40 = load_map_40()
+    correct = 0
+    for start, goal, answer_path in MAP_40_ANSWERS:
+        path = shortest_path_function(map_40, start, goal).path
+        if path == answer_path:
+            correct += 1
+            print("For start:", start, 
+                  "Goal:     ", goal,
+                  "Your path:", path,
+                  "Correct:  ", answer_path)
+        else:
+             print("Error Testing faild !!!" )
+    if correct == len(MAP_40_ANSWERS):
+        print("All tests pass ")
+    else:
+        print("Only passed", correct, "/", len(MAP_40_ANSWERS), "test cases")
+    
\ No newline at end of file