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I used return, however the recursion does not end. help me please

I am doing a question that gives me a start coordinate, a end coordinate and the number of times of moving.Every time you can add 1 or minus 1 to x or y coordinate based on previous coordinate and the number of moving limit the time the coordinate can move. At last, I need to identify whether there is a possibility to get to the end coordinate
I decide to use recursion to solve this problem however, it does not end even if I wrote return inside a if else statement. Do you mind to take a look at it.
This is the code
# https://cemc.uwaterloo.ca/contests/computing/2017/stage%201/juniorEF.pdf
# input
start = input()
end = input()
count = int(input())
coo_end = end.split(' ')
x_end = coo_end[0]
y_end = coo_end[1]
end_set = {int(x_end), int(y_end)}
#processing
coo = start.split(' ')
x = int(coo[0])
y = int(coo[1])
change_x = x
change_y = y
sum = x + y+count
set1 = set()
tim = 0
timer = 0
ways = 4** (count-1)
def elit(x, y, tim,timer, ways = ways):
print(tim,timer)
tim = tim +1
co1 = (x, y+1)
co2 = (x+1, y)
co3 = (x, y-1)
co4 = (x-1, y)
if tim == count:
tim =0
set1.add(co1)
set1.add(co2)
set1.add(co3)
set1.add(co4)
print(timer)
timer = timer +1
if timer == ways:
print('hiii')
return co1, co2, co3, co4 #### this is the place there is a problem
elit(co1[0],co1[1],tim,timer)
elit(co2[0],co2[1],tim,timer)
elit(co3[0],co3[1],tim, timer)
elit(co4[0],co4[1],tim, timer)
#print(elit(change_x,change_y,tim)) - none why
elit(change_x,change_y,tim, timer)
#print(list1)
for a in set1:
if end_set != a:
answer = 'N'
continue
else:
answer = "Y"
break
print(answer)
In addition, if you have any suggestions about writing this question, do you mind to tell me since I am not sure I am using the best solution.
one of example is
Sample Input
3 4 (start value)
3 3 (end value)
3 (count)
Output for Sample Input
Y
Explanation
One possibility is to travel from (3, 4) to (4, 4) to (4, 3) to (3, 3).
the detailed question can be seen in this file https://cemc.uwaterloo.ca/contests/computing/2017/stage%201/juniorEF.pdf
It is question 3. Thank you
thank you guys

the function is returning properly however by the time you reach the recursive depth to return anything you have called so many instances of the function that it seems like its in an infinite loop
when you call elite the first time the function calls itself four more times, in the example you have given timer is only incremented every 3 cycles and the function only return once timer hits 16 thus the function will need to run 48 times before returning anything and each time the function will be called 4 more times, this exponential growth means for this example the function will be called 19807040628566084398385987584 times, which depending on your machine may well take until the heat death of the universe
i thought i should add that i think you have somewhat over complicated the question, on a grid to get from one point to another the only options are the minimum distance or that same minimum with a diversion that must always be a multiple of 2 in length, so if t the movement is at least the minimum distance or any multiple of 2 over the result should be 'Y', the minimum distance will just be the difference between the coordinates on each axis this can be found by add in the difference between the x and y coordinates
abs(int(start[0]) - int(end[0])) + abs(int(start[1]) -int(end[1]))
the whole function therefore can just be:
def elit():
start = input('start: ').split(' ')
end = input('end: ').split(' ')
count = int(input('count: '))
distance = abs(int(start[0]) - int(end[0])) + abs(int(start[1]) -int(end[1]))
if (count - distance) % 2 == 0:
print('Y')
else:
print('N')
input:
3 4
3 3
3
output:
Y
input:
10 4
10 2
5
output:
N

Python Filtering a Point Cloud with PhotoScan Based on a Threshold Value - basic python help needed

I'm trying to implement a filter with Python to sort out the points on a point cloud generated by Agisoft PhotoScan. PhotoScan is a photogrammetry software developed to be user friendly but also allows to use Python commands through an API.
Bellow is my code so far and I'm pretty sure there is better way to write it as I'm missing something. The code runs inside PhotoScan.
Objective:
Selecting and removing 10% of points at a time with error within defined range of 50 to 10. Also removing any points within error range less than 10% of the total, when the initial steps of selecting and removing 10% at a time are done. Immediately after every point removal an optimization procedure should be done. It should stop when no points are selectable or when selectable points counts as less than 1% of the present total points and it is not worth removing them.
Draw it for better understanding:
Actual Code Under Construction (3 updates - see bellow for details):
import PhotoScan as PS
import math
doc = PS.app.document
chunk = doc.chunk
# using float with range and that by setting i = 1 it steps 0.1 at a time
def precrange(a, b, i):
if a < b:
p = 10**i
sr = a*p
er = (b*p) + 1
p = float(p)
for n in range(sr, er):
x = n/p
yield x
else:
p = 10**i
sr = b*p
er = (a*p) + 1
p = float(p)
for n in range(sr, er):
x = n/p
yield x
"""
Determine if x is close to y:
x relates to nselected variable
y to p10 variable
math.isclose() Return True if the values a and b are close to each other and
False otherwise
var is the tolerance here setted as a relative tolerance:
rel_tol is the relative tolerance – it is the maximum allowed difference
between a and b, relative to the larger absolute value of a or b. For example,
to set a tolerance of 5%, pass rel_tol=0.05. The default tolerance is 1e-09,
which assures that the two values are the same within about 9 decimal digits.
rel_tol must be greater than zero.
"""
def test_isclose(x, y, var):
if math.isclose(x, y, rel_tol=var): # if variables are close return True
return True
else:
False
# 1. define filter limits
f_ReconstUncert = precrange(50, 10, 1)
# 2. count initial point number
tiePoints_0 = len(chunk.point_cloud.points) # storing info for later
# 3. call Filter() and init it
f = PS.PointCloud.Filter()
f.init(chunk, criterion=PS.PointCloud.Filter.ReconstructionUncertainty)
a = 0
"""
Way to restart for loop!
should_restart = True
while should_restart:
should_restart = False
for i in xrange(10):
print i
if i == 5:
should_restart = True
break
"""
restartLoop = True
while restartLoop:
restartLoop = False
for count, i in enumerate(f_ReconstUncert): # for each threshold value
# count points for every i
tiePoints = len(chunk.point_cloud.points)
p10 = int(round((10 / 100) * tiePoints, 0)) # 10% of the total
f.selectPoints(i) # selects points
nselected = len([p for p in chunk.point_cloud.points if p.selected])
percent = round(nselected * 100 / tiePoints, 2)
if nselected == 0:
print("For threshold {} there´s no selectable points".format(i))
break
elif test_isclose(nselected, p10, 0.1):
a += 1
print("Threshold found in iteration: ", count)
print("----------------------------------------------")
print("# {} Removing points from cloud ".format(a))
print("----------------------------------------------")
print("# {}. Reconstruction Uncerntainty:"
" {:.2f}".format(a, i))
print("{} - {}"
" ({:.1f} %)\n".format(tiePoints,
nselected, percent))
f.removePoints(i) # removes points
# optimization procedure needed to refine cameras positions
print("--------------Optimizing cameras-------------\n")
chunk.optimizeCameras(fit_f=True, fit_cx=True,
fit_cy=True, fit_b1=False,
fit_b2=False, fit_k1=True,
fit_k2=True, fit_k3=True,
fit_k4=False, fit_p1=True,
fit_p2=True, fit_p3=False,
fit_p4=False, adaptive_fitting=False)
# count again number of points in point cloud
tiePoints = len(chunk.point_cloud.points)
print("= {} remaining points after"
" {} removal".format(tiePoints, a))
# reassigning variable to get new 10% of remaining points
p10 = int(round((10 / 100) * tiePoints, 0))
percent = round(nselected * 100 / tiePoints, 2)
print("----------------------------------------------\n\n")
# restart loop to investigate from range start
restartLoop = True
break
else:
f.resetSelection()
continue # continue to next i
else:
f.resetSelection()
print("for loop didnt work out")
print("{} iterations done!".format(count))
tiePoints = len(chunk.point_cloud.points)
print("Tiepoints 0: ", tiePoints_0)
print("Tiepoints 1: ", tiePoints)
Problems:
A. Currently I'm stuck on an endless processing because of a loop. I know it's about my bad coding. But how do I implement my objective and get away with the infinite loops? ANSWER: Got the code less confusing and updated above.
B. How do I start over (or restart) my search for valid threshold values in the range(50, 20) after finding one of them? ANSWER: Stack Exchange: how to restart a for loop
C. How do I turn the code more pythonic?
IMPORTANT UPDATE 1: altered above
Using a better range with float solution adapted from stackoverflow: how-to-use-a-decimal-range-step-value
# using float with range and that by setting i = 1 it steps 0.1 at a time
def precrange(a, b, i):
if a < b:
p = 10**i
sr = a*p
er = (b*p) + 1
p = float(p)
return map(lambda x: x/p, range(sr, er))
else:
p = 10**i
sr = b*p
er = (a*p) + 1
p = float(p)
return map(lambda x: x/p, range(sr, er))
# some code
f_ReconstUncert = precrange(50, 20, 1)
And also using math.isclose() to determine if selected points are close to the 10% selected points instead of using a manual solution through assigning new variables. This was implemented as follows:
"""
Determine if x is close to y:
x relates to nselected variable
y to p10 variable
math.isclose() Return True if the values a and b are close to each other and
False otherwise
var is the tolerance here setted as a relative tolerance:
rel_tol is the relative tolerance – it is the maximum allowed difference
between a and b, relative to the larger absolute value of a or b. For example,
to set a tolerance of 5%, pass rel_tol=0.05. The default tolerance is 1e-09,
which assures that the two values are the same within about 9 decimal digits.
rel_tol must be greater than zero.
"""
def test_threshold(x, y, var):
if math.isclose(x, y, rel_tol=var): # if variables are close return True
return True
else:
False
# some code
if test_threshold(nselected, p10, 0.1):
# if true then a valid threshold is found
# some code
UPDATE 2: altered on code under construction
Minor fixes and got to restart de for loop from beginning by following guidance from another Stack Exchange post on the subject. Have to improve the range now or alter the isclose() to get more values.
restartLoop = True
while restartLoop:
restartLoop = False
for i in range(0, 10):
if condition:
restartLoop = True
break
UPDATE 3: Code structure to achieve listed objectives:
threshold = range(0, 11, 1)
listx = []
for i in threshold:
listx.append(i)
restart = 0
restartLoop = True
while restartLoop:
restartLoop = False
for idx, i in enumerate(listx):
print("do something as printing i:", i)
if i > 5: # if this condition restart loop
print("found value for condition: ", i)
del listx[idx]
restartLoop = True
print("RESTARTING LOOP\n")
restart += 1
break # break inner while and restart for loop
else:
# continue if the inner loop wasn't broken
continue
else:
continue
print("restart - outer while", restart)

Spawning objects in groups when the first object of the group was spawned randomly Python

I'm currently doing a project, and in the code I have, I'm trying to get trees .*. and mountains .^. to spawn in groups around the first tree or mountain which is spawned randomly, however, I can't figure out how to get the trees and mountains to spawn in groups around a single randomly generated point. Any help?
grid = []
def draw_board():
row = 0
for i in range(0,625):
if grid[i] == 1:
print("..."),
elif grid[i] == 2:
print("..."),
elif grid[i] == 3:
print(".*."),
elif grid[i] == 4:
print(".^."),
elif grid[i] == 5:
print("[T]"),
else:
print("ERR"),
row = row + 1
if row == 25:
print ("\n")
row = 0
return

There's a number of ways you can do it.
Firstly, you can just simulate the groups directly, i.e. pick a range on the grid and fill it with a specific figure.
def generate_grid(size):
grid = [0] * size
right = 0
while right < size:
left = right
repeat = min(random.randint(1, 5), size - right) # *
right = left + repeat
grid[left:right] = [random.choice(figures)] * repeat
return grid
Note that the group size need not to be uniformly distributed, you can use any convenient distribution, e.g. Poisson.
Secondly, you can use a Markov Chain. In this case group lengths will implicitly follow a Geometric distribution. Here's the code:
def transition_matrix(A):
"""Ensures that each row of transition matrix sums to 1."""
copy = []
for i, row in enumerate(A):
total = sum(row)
copy.append([item / total for item in row])
return copy
def generate_grid(size):
# Transition matrix ``A`` defines the probability of
# changing from figure i to figure j for each pair
# of figures i and j. The grouping effect can be
# obtained by setting diagonal entries A[i][i] to
# larger values.
#
# You need to specify this manually.
A = transition_matrix([[5, 1],
[1, 5]]) # Assuming 2 figures.
grid = [random.choice(figures)]
for i in range(1, size):
current = grid[-1]
next = choice(figures, A[current])
grid.append(next)
return grid
Where the choice function is explained in this StackOverflow answer.

Can't assign set to variable python

Hi I was wondering if this was possible at all, since I tried it but my variable was always empty. In my project, I'm tracking a static object and a laser pointer via a PiCamera on my raspberry pi, and I calculate the centroids of their contours as (smallx,smally) and (small2x,small2y) respectively.
I use the difference between their coordinates to see if the pointer should go up, down, left, or right in order to meet the 1st static object. After that, it'll choose a direction betweeen 1 through 4 to move, because my direction controls aren't perfectly on an x-y axis and are slanted.
I left the controls and the contour finding out from here and shortened my total code just so that you wouldn't be met with a giant pile of slop to sort through.
EDIT: I' don't think with my understanding I could provide something runnable without posting a couple hundred lines and my little device, but I'll boil it down and post the exact portion of my code where this is relevant. Running Python 2.7.3, using opencv2.4.10
Code:
#import libraries like picamera and opencv
#set empty variables like:
up = down = left = right = set()
smallx = smally = small2x = small2y = 0
#etc etc
with picamera.PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as rawCapture:
#Calibrate my controls with the camera. updates the up, down, left, and right sets.
with picamera.PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as rawCapture:
# Take pictures, threshold them, find contours, append their arrays to list
if len(cnts)>0: #If any objects were identified
contm = sorted(smalList, key=lambda tup: tup[1])
smallest = cnts[smalList[0][0]] #**Take smallest object(my static object)**
smallM = cv2.moments(smallest)
smallx = int(smallM['m10']/smallM['m00']) #**Calculate xcoord**
smally = int(smallM['m01']/smallM['m00']) #**Calculate ycoord**
cv2.line(frame, (smallx,smally), (smallx,smally), 1, 8,0) #Draws centroid
# print(len(cnts))
if len(cnts)==2: #If only 2 objects were identified
smallester = cnts[smalList[1][0]] #** Take pointer object **
small2 = cv2.moments(smallester)
small2x = int(small2['m10']/small2['m00']) #**Calculate xcoord**
small2y = int(small2['m01']/small2['m00']) #**Calculate ycoord**
x = small2x - smallx
y = small2y - smally
print x #These prints return a value
print y
if x < 0: #Difference = Pointer - Object
s1 = right
if x >0:
s1 = left
if y < 0:
s2 = down
if y >0:
s2 = up
print s1, s2 #set([]),set([])
print up,down,left,right #set([1,2]),set([3,4]),set([1,4]),set([2,3])
selecty = s1&s2 #set([])
#Tell the pointer where to go
Should I even be using sets?

Use s1 = s2 = set() instead of = 0.
As for your second question, there are probably better and more known ways to go around your problem. For example, using bit logic:
right = 1
up = 2
left = 4
down = 8
select = 0
if (small2x - smallx) < 0:
select |= right
if (small2x - smallx) >0:
select |= left
if (small2y - smally) < 0:
select |= down
if (small2y - smally) >0:
select |= up
print(select)
print("You chose %s%s%s%s" %("UP " if select & up else "",
"DOWN " if select & down else "",
"LEFT " if select & left else "",
"RIGHT" if select & right else ""))
#Do things after

How to deal with very big Bitboards

I'm working on a 2-player board game (e.g. connect 4), with parametric board size h, w. I want to check for winning condition using hw-sized bitboards.
In game like chess, where board size is fixed, bitboards are usually represented with some sort of 64-bit integer. When h and w are not constant and maybe very big (let's suppose 30*30) are bitboards a good idea? If so, are the any data types in C/C++ to deal with big bitboards keeping their performances?
Since I'm currently working on python a solution in this language is appreciated too! :)
Thanks in advance

I wrote this code while ago just to play around with the game concept. There is no intelligence behaviour involve. just random moves to demonstrate the game. I guess this is not important for you since you are only looking for a fast check of winning conditions. This implementation is fast since I did my best to avoid for loops and use only built-in python/numpy functions (with some tricks).
import numpy as np
row_size = 6
col_size = 7
symbols = {1:'A', -1:'B', 0:' '}
def was_winning_move(S, P, current_row_idx,current_col_idx):
#****** Column Win ******
current_col = S[:,current_col_idx]
P_idx= np.where(current_col== P)[0]
#if the difference between indexes are one, that means they are consecutive.
#we need at least 4 consecutive index. So 3 Ture value
is_idx_consecutive = sum(np.diff(P_idx)==1)>=3
if is_idx_consecutive:
return True
#****** Column Win ******
current_row = S[current_row_idx,:]
P_idx= np.where(current_row== P)[0]
is_idx_consecutive = sum(np.diff(P_idx)==1)>=3
if is_idx_consecutive:
return True
#****** Diag Win ******
offeset_from_diag = current_col_idx - current_row_idx
current_diag = S.diagonal(offeset_from_diag)
P_idx= np.where(current_diag== P)[0]
is_idx_consecutive = sum(np.diff(P_idx)==1)>=3
if is_idx_consecutive:
return True
#****** off-Diag Win ******
#here 1) reverse rows, 2)find new index, 3)find offest and proceed as diag
reversed_rows = S[::-1,:] #1
new_row_idx = row_size - 1 - current_row_idx #2
offeset_from_diag = current_col_idx - new_row_idx #3
current_off_diag = reversed_rows.diagonal(offeset_from_diag)
P_idx= np.where(current_off_diag== P)[0]
is_idx_consecutive = sum(np.diff(P_idx)==1)>=3
if is_idx_consecutive:
return True
return False
def move_at_random(S,P):
selected_col_idx = np.random.permutation(range(col_size))[0]
#print selected_col_idx
#we should fill in matrix from bottom to top. So find the last filled row in col and fill the upper row
last_filled_row = np.where(S[:,selected_col_idx] != 0)[0]
#it is possible that there is no filled array. like the begining of the game
#in this case we start with last row e.g row : -1
if last_filled_row.size != 0:
current_row_idx = last_filled_row[0] - 1
else:
current_row_idx = -1
#print 'col[{0}], row[{1}]'.format(selected_col,current_row)
S[current_row_idx, selected_col_idx] = P
return (S,current_row_idx,selected_col_idx)
def move_still_possible(S):
return not (S[S==0].size == 0)
def print_game_state(S):
B = np.copy(S).astype(object)
for n in [-1, 0, 1]:
B[B==n] = symbols[n]
print B
def play_game():
#initiate game state
game_state = np.zeros((6,7),dtype=int)
player = 1
mvcntr = 1
no_winner_yet = True
while no_winner_yet and move_still_possible(game_state):
#get player symbol
name = symbols[player]
game_state, current_row, current_col = move_at_random(game_state, player)
#print '******',player,(current_row, current_col)
#print current game state
print_game_state(game_state)
#check if the move was a winning move
if was_winning_move(game_state,player,current_row, current_col):
print 'player %s wins after %d moves' % (name, mvcntr)
no_winner_yet = False
# switch player and increase move counter
player *= -1
mvcntr += 1
if no_winner_yet:
print 'game ended in a draw'
player = 0
return game_state,player,mvcntr
if __name__ == '__main__':
S, P, mvcntr = play_game()
let me know if you have any question
UPDATE: Explanation:
At each move, look at column, row, diagonal and secondary diagonal that goes through the current cell and find consecutive cells with the current symbol. avoid scanning the whole board.
extracting cells in each direction:
column:
current_col = S[:,current_col_idx]
row:
current_row = S[current_row_idx,:]
Diagonal:
Find the offset of the desired diagonal from the
main diagonal:
diag_offset = current_col_idx - current_row_idx
current_diag = S.diagonal(offset)
off-diagonal:
Reverse the rows of matrix:
S_reversed_rows = S[::-1,:]
Find the row index in the new matrix
new_row_idx = row_size - 1 - current_row_idx
current_offdiag = S.diagonal(offset)