Python | Search Algorithm just taking up 100% of CPU and never returning anything

I am trying to write a search algorithm that takes in a start point and then return the path to the end point, I originally tried just doing it via some nested for loops and a list of lists so that I could just loop through and try to find a path but the RAM requirements convinced me to try it using a class-based system. However, all it is doing is taking like 2gb of RAM and 100% of one of my CPU cores and just sitting there without exiting. If anyone sees a problem in my code, any help would be greatly appreciated.

``````import csv
import math
from multiprocessing import Process
from rplidar import RPLidar
import heapq

lidar = RPLidar('/dev/ttyUSB0')
file = "lidar01.csv"

def calc_offset():
# take in argos ros data and calculate offset
x_offset = 0
y_offset = 0
return x_offset, y_offset

offset_x, offset_y = calc_offset()
if x >= 0 and y >= 0:
x = abs(x + 12000 + offset_x)
y = abs(y + offset_y)
return x,y
elif x < 0 and y >= 0:
x = abs(x - 12000 + offset_x)
y = abs(y + offset_x)
return x,y
elif x < 0 and y < 0:
x = abs(x - 12000 + offset_x)
y = abs(y - 12000 + offset_y)
return x,y
elif x >= 0 and y < 0:
x = abs(x + 12000 + offset_x)
y = abs(y - 12000 + offset_y)
return x,y

def scan():
try:
for scan in enumerate(lidar.iter_scans()):
list_version_data = list(scan)
for data in list_version_data:
if isinstance(data, list):
for indiv_data_points in data:
if isinstance(indiv_data_points, tuple):
list_indiv_data_points = list(indiv_data_points)
list_indiv_data_points.pop(0)
angle = list_indiv_data_points[0]
distance = list_indiv_data_points[1]
length = distance
angle = angle
x,y = (length * math.cos(angle)), (length * math.sin(angle))
x = int(x)
y = int(y)
with open(file=file, mode="w") as f:
writer = csv.writer(f)
writer.writerow([new_x,new_y])

except Exception as e:
print(e)
pass

def eliminate_duplicates():
unique_coords = set()
with open(file, 'r') as f:
coord = (row[0], row[1])
if coord not in unique_coords:

with open(file, 'w') as f:
writer = csv.writer(f)
for coord in unique_coords:
writer.writerow(coord)

# create the node class that takes in the individual data points and creates a node for the nav graph
class Node:
def __init__(self, x, y):
self.x = x
self.y = y
self.neighbors = []
self.parent = None

def __eq__(self, other):
return self.x == other.x and self.y == other.y

def __lt__(self, other):
return self.f < other.f

def scan_eliminate_duplicates():
scan_process = Process(target=scan)
eliminate_duplicates_process = Process(target=eliminate_duplicates)
scan_process.start()
scan_process.join()
eliminate_duplicates_process.start()
eliminate_duplicates_process.join()

def find_path(start, end, nodes):
open_set = []
closed_set = set()
start.f = 0
heapq.heappush(open_set, start)

while open_set:
current_node = heapq.heappop(open_set)
if current_node == end:
print(f"Path found: {0}".format(construct_path(current_node)))
return construct_path(current_node)

for neighbor in current_node.neighbors:
if neighbor in closed_set:
continue
tentative_g = current_node.f + 1
if neighbor not in open_set or tentative_g < neighbor.f:
neighbor.parent = current_node
neighbor.f = tentative_g
if neighbor not in open_set:
heapq.heappush(open_set, neighbor)

return None

def construct_path(node):
path = []
while node.parent:
path.append((node.x, node.y))
node = node.parent
return path[::-1]

if __name__ == "__main__":
scan_elim_dupl_process = Process(target=scan_eliminate_duplicates)
nodes = []
with open(file, "r") as f:
node = Node(int(float(row[0])), int(float(row[1])))
nodes.append(node)
# set start and end nodes
start = Node(3201, 3201)
end = Node(23000, 23000)
# connect the nodes to their neighbors
for i, node in enumerate(nodes):
for j in range(i+1, len(nodes)):
if abs(node.x - nodes[j].x) <= 1 and abs(node.y - nodes[j].y) <= 1:
node.neighbors.append(nodes[j])
nodes[j].neighbors.append(node)
find_path_process = Process(target=find_path, args=(start, end, nodes))
scan_elim_dupl_process.start(), find_path_process.start()
scan_elim_dupl_process.join(), find_path_process.join()
``````

CSV Data(example):

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``````
2023-01-20 00:30:14
Looking at your code it seems counterintuitive (or backwards) 1) typically a Node class references a `next` rather than a `parent` because you would have the terminal as a `null` at the end of a search (though you may be resolving this with your code) 2) you can look at which part of the code is not completing by running a simple case and putting in logging. It should be quite straightforward to create a minimal graph to search e.g. with 4/5 points - because I have a feeling that your code would not terminate even under those conditions.
``````for scan in enumerate(lidar.iter_scans()):
`You need to update your code to have a non-error exit condition.` Does this mean specifically for the `scan` function or more generally? Because I think that there is one for the more general function? It is supposed to print that a pth is found but it just never happens either way.