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How to change value to zero but not in the first iteration of a loop

I am trying to implement turning of front wheels in PyBox2D, for now, I was able to make it turn left/right but I am not able to stop it when the angle reaches zero (to make the car to go straight)
My goal is to stop turning when the angle of a wheel reaches zero or value similar to zero, but not on the beginning (sometimes when the angles are zero they do not move at all, and if possible I would like to make it independent from pressing key on a keyboard (moving those two nested if statements out of the if keyboard_is_pressed(hotkey) part did not help
I hope I made myself clear and thank you very much for any help
EDIT I tried to implement solution given in the answer, it kind of worked but I tried to improve it and now I am stuck again, the wheels turn, but when they return to their initial position they stop moving. One of problems can be that when I press "a" or "d" key my variable self.ticking changes by more than just one, because I am not able to press the key for such a short period of time.
variable self.on_the_beginning is equivalent to on_starting_race from the answer below:
def control(self): # This makes control independent from visualisation
#Front left suspension: index 2
#Front right suspension: index 3
print(self.ticking)
if keyboard.is_pressed("a"):
self.suspensions[2].motorSpeed = -5
self.suspensions[3].motorSpeed = -5
self.ticking -= 1
if keyboard.is_pressed("d"):
self.suspensions[2].motorSpeed = 5
self.suspensions[3].motorSpeed = 5
self.ticking += 1
if self.ticking <= -3:
self.ticking = -3
self.on_the_beginning = True
elif self.ticking >= 3:
self.ticking = 3
self.on_the_beginning = True
if np.radians(-5) <= self.tires[2].wheel.angle <= np.radians(5) and self.on_the_beginning == True and self.ticking !=0:
self.suspensions[2].motorSpeed = 0
self.suspensions[3].motorSpeed = 0
self.tires[2].SetAngularVelocity = 0
self.tires[3].SetAngularVelocity = 0
self.ticking = 0
on_the_beginning = False

If i understand correctly, you can have a variable, say on_starting_race, set to false, then check whenever it is above a set number (say, when it's above 10 you know for a fact that the race has already started and the car moved at least for a few seconds), then change that value to True, now add an if statement to determine whether the value is close to 0 (say val<5) AND on_starting_race is True.
There might be a more elegant way, but this is pretty straight forward(assuming you check the state of the car every frame or a set period of time).

Sorry, because I am not 100% sure of your problem without the whole code.
I think the solution could be using an input parameter in you function, let's say first_run, that you can control from inside and outside the function.
def control(self, first_run=True):
This way, you may start the race (from your main program) setting first_run to True, so you don't care about the angle. And use this same function setting first_run to False the rest of the times.
You can also use a self.first_run variable that you may set to True in the init, then setting self.first_run to False if it is True (which is really the first time you use your control() function).

Why my solution is unable to solve 8puzzle problem for boards that require more than 1 move?

I am trying to solve 8 puzzle problem in python given here in this assignment -https://www.cs.princeton.edu/courses/archive/fall12/cos226/assignments/8puzzle.html
My goal state is a little different from what is mentioned in the assignment -
#GOAL STATE
goal_state = [[0,1,2],[3,4,5],[6,7,8]]
The buggy part, it seems, is the isSolvable function. It is implemented correctly but while testing the board, it considers the goal state to be the one in which relative order is maintained and blank can be anywhere. So it might be the case that a board is solvable but it might not lead to the current defined goal state. So I am unable to think of a method in which I can test for all the possible goal states while running the solver function *
Also, my solver function was wrongly implemented. I was only considering the neighbor which had the minimum manhattan value and when I was hitting a dead end, I was not considering other states. This can be done by using a priority queue. I am not exactly sure as to how to proceed to implement it. I have written a part of it(see below) which is also kind of wrong as I not pushing the parent into the heap. Kindly provide me guidance for that.
Here is my complete code -
https://pastebin.com/q7sAKS6a
Updated code with incomplete solver function -
https://pastebin.com/n4CcQaks
I have used manhattan values to calculate heuristic values and hamming value to break the tie.
my isSolvable function, manhattan function and solver function:
isSolvable function -
#Conditions for unsolvability -->
#https://www.geeksforgeeks.org/check-instance-8-puzzle-solvable/
def isSolvable(self):
self.one_d_array = []
for i in range(0,len(self.board)):
for j in range(0,len(self.board)):
self.one_d_array.append(self.board[i][j])
inv_count = 0
for i in range(0,len(self.one_d_array)-1):
for j in range(i+1, len(self.one_d_array)):
if (self.one_d_array[i] != 0 and self.one_d_array[j] != 0 and self.one_d_array[i] > self.one_d_array[j]):
inv_count = inv_count + 1
if(inv_count % 2 == 0):
print("board is solvable")
return True
else:
print("board is not solvable")
return False
Manhattan function
def manhattan_value(self,data=None):
manhattan_distance = 0
for i in range(0,len(data)):
for j in range(0,len(data)):
if(data[i][j] != self.goal_state[i][j] and data[i][j] != 0):
#correct position of the element
x_goal , y_goal = divmod(data[i][j],3)
manhattan_distance = manhattan_distance + abs(i-x_goal) + abs(j-y_goal)
return manhattan_distance
Updated Solver function
#implement A* algorithm
def solver(self):
moves = 0
heuristic_value = []
prev_state = []
curr_state = self.board
output = []
heap_array = []
store_manhattan_values = []
if(curr_state == self.goal_state):
print("goal state reached!")
print(curr_state)
print("number of moves required to reach goal state --> {}".format(moves))
else:
while(True):
min_heuristic_value = 99999999999
min_pos = None
moves = moves + 1
output = self.get_neighbours(curr_state)
for i in range(len(output)):
store_manhattan_values.append([self.manhattan_value(output[i]),i])
#print(store_manhattan_values)
for i in range(len(store_manhattan_values)):
heapq.heappush(heap_array,store_manhattan_values[i])
#print(heap_array)
#print(heapq.heappop(heap_array)[1])
#if(moves > 1):
# return
return
Please refer to the PASTEBIN link for complete code and all the references (https://pastebin.com/r7TngdFc).
Updated code with incomplete solver function -
https://pastebin.com/n4CcQaks
In the given link for my code (based on my tests and debugging so far) -
These functions are working correctly - manhatten_value, hamming_value, append_in_list, get_neighbours
What does these functions do -
isSolvable - tells if the board can be solved or not
manhattan_value - calculates the manhattan value of the board passed to it.
hamming_value - calculates the hamming value of the board passed to it.
append_in_list - helper function for getting neighbours. It swaps values then save the resultant state in an array and then reswaps them to return to original position for further swapping and getting other possible states.
get_neighbours - gets all the possible neighbors which can be formed by swapping places with blank element(0 element).
solver - implements the A* algorithm
I am unable to find my mistake. Kindly guide me in this problem. Thank you in advance for your help!
I am apologizing in advance as I am unable to produce a minimal version of my code for this problem. I can not think of any way to use all the functions and produce a minimal version of the code.

(Note, this answer is different than the earlier revision about which many of the comments below were relating to.)
I don't see how the current code implements a queue. It seems like the while loop in the solver picks one new board state each time from a list of possible moves, then considers the next list generated by this new board state.
On the other hand, a priority queue, from what I understand, would have all the (valid) neighbours from the current board state inserted into it and prioritised such that the next chosen board state to be removed from the queue and examined will be the one with highest priority.
(To be completely sure in debugging, I might add a memoisation to detect if the code ends up also revisiting board states -- ah, on second thought, I believe the stipulation in the assignment description that the number of current moves be added to the priority assignment would rule out the same board state being revisited if the priority queue is correctly observed, so memoisation may not be needed.)

Computer decision mechanism

I have 5 players where they throw dice. We cannot use any external input such as onclick action or something.
How do I make the computer decide whether it is good to stop throwing? The stopping criteria is either you didn't throw 1,5 or straight or triples or higher. Everything counts as a point and if you hit something, lets say triple sixes you now can decide whether you throw again, but without these three dice. But if you fail to hit anything on the next throw, you lose every point you've got in the section. Or keep the sixes, which gives you 600 points.
def game(length, output = True):
round_no = 0
avg_pts = 0
player_points[0,0,0,0,0]
while output:
round_no += 1
for i in range 5:
lock = true
while lock:
dice_throw(number_of_throws)
def dice_throw(number_of_throws):
throw_values = []
for i in range(number_of_throws):
throw_values.append(randint(1,6))
throw_values.sort()
for i in range(throw_values)
How can I make a mechanism that decides if its good to continue throwing or not?

python, how to write an iterative function

I am quering a database for some paramaters which depend on a attribute called count! count can be incremented incase the 1st query does not return anything. Here is a sample code
sls = {(213.243, 55.556): {}, (217.193, 55.793): {}, (213.403, 55.369): {}}
for key in sls.keys:
if not sls[key]:
ra, dec = key[0], key[1]
search_from_sourcelist(sl, ra,dec)
count = 1
def search_from_sourcelist(sl, ra,dec):
dist = count/3600.0
sls[(ra,dec)] = sl.sources.area_search(Area=(ra,dec,dist))
return
Incase i run the method search_from_sourcelist, and it doesnt return anything, i would like to increment count, and do the query again. This is to be done for all keys in sls dictionary, untill all the keys have a value!!

Here is the most fundamental recursive function
def countdown(n):
if n == 0:
return "Blastoff"
else:
print "T minus %s" % n
return countdown(n-1)
You will notice that countdown returns itself with a modified argument, in this case n but -1, so if you actually followed this all the way through you would get
(-> indicates a call)
countdown(5) -> countdown(4) -> countdown(3) -> countdown(2) -> countdown(1) -> countdown(0) #stop
So now you understand what a recursive function looks like you realize you never actually return a call of your own function, thus your code is not recursive
We use recursion because we want to boil a task down to its simplest form then work from there, so a good example of this would be the mcnuggets problem. So you need to tell us what you are trying to achieve and how it can be made a smaller problem (or more importantly why.) Are you sure you cannot do this iteratively? remember you don't want to blow your stack depth because python is NOT tail recursive by standard

Recursion is useful when you find a way to reduce the initial problem to a "smaller version of itself".
The standard example is the factorial function
def fac(n):
return n * fac(n-1) if n > 1 else 1
Here you reduce the problem of calculating the factorial of n to calculating the factorial of n-1.
In your code there is no such "reduction". You just increment a value and start the same problem over again. Thus, I recommend you solve it iteratively.

I'm not sure that you need a recursive algorithm for this.
Incase i run the method search_from_sourcelist, and it doesnt return anything, i would like to increment count, and do the query again. This can be done with a while loop as follows:
for key, value in sls.iteritems():
if not value:
ra, dec = key[0], key[1]
count = 1
while not search_from_sourcelist(sls, ra, dec):
count += 1
But if you really do want to do this recursively, you can do it as follows, leave a comment and I'll write it up.
Further, you should look into your search_from_sourcelist function, as it always returns None

python list Index out of range error

I am working on a python tetris game that my proffessor assigned for the final project of a concepts of programming class. I have got just about everything he wanted to work on it at this point but I am having a slight problem with one part of it. Whenever I start moving pieces left and right I keep getting "index out of range error". This only happens when it is up against a piece. Here are the culprits that are giving me grief.
def clearRight(block=None):
global board, activeBlock, stackedBlocks
isClear = True
if(block == None):
block = activeBlock
if(block != None):
for square in block['squares']:
row = square[1]
col = square[0]+1
if(col >= 0 and stackedBlocks[row][col] !=None):
isClear=False
return isClear
def clearLeft(block=None):
global board, activeBlock, stackedBlocks
isClear = True
if(block == None):
block = activeBlock
if(block != None):
for square in block['squares']:
row = square[1]
col = square[0]-1
if(col >= 0 and stackedBlocks[row][col] !=None):
isClear=False
return isClear
I am not looking to get anyone to fix it for me, I'm only looking for tips on how to fix it myself. Thanks in advance for any help that is given.

There a typo that would cause that problem in the first method.
When you're checking each cell in the block shifted one right, you don't check if they are off the grid.
if (col >= 0 and ...)
probably should be
if (col < num_cols and ...)
I also agree with CrazyDrummer, make a generic clear function
Spoilers ...
def clear(x_offset, block=None):
if not block:
block = activeBlock
if not block: return True
for x,y in block:
x += x_offset
if not (0 <= x < num_cols) or stackedBlocks[x, y]:
return False
return True

Look at what's different when you're getting the exception. Try printing out program state information to help you zero in. There's only one place where you access an array with variable indexes, so you can narrow your search radius a bit.
Separate suggestion: Make a generic clear that takes determines what direction you want to clear from by the parameters.
I highly recommend the book debugging rules!, it will aid you in searching out and properly fixing problems. :D