I am trying to fill the area between multiple shapes. The shapes are concave and one shape is always inside the other. The goal is to convert all pixels between the two shapes to 1 and all others to 0.
The single lines look like this :
My goal is to fill both shapes and then substract them. So that the end result only shows the area where they do not overlap.
Like this :
However as you can see the result is not very accurate and this example is one of the good images.
I think the problem is my code to fill the shapes.
Code :
def line_to_area(mask):
derivative = np.diff(mask)
states = ["firstorlast","apart","touching"]
for idx_row, row in enumerate(derivative):
toggle = False
counter = 0
state = states[1]
awareness = collections.Counter(row)[1.0]
if awareness % 2 != 0 and awareness >= 3:
state = states[2]
elif awareness % 2 != 0 and awareness ==1:
state = states[0]
for idx_pixel, pixel in enumerate(row):
if state == "firstorlast":
break
elif state == "touching" and counter == (awareness-1):
break
if pixel == 0 and toggle == False:
continue
elif pixel == 1 and toggle == False:
toggle = True
counter = counter + 1
elif pixel == 0 and toggle == True:
mask[(idx_row, idx_pixel)] = 1
elif pixel == 1 and toggle == True:
mask[(idx_row, idx_pixel)] = 1
toggle = False
return mask
The input to the function is a 2dim numpy array with the shape (208,208).
The idea is to go through every row of the array and use the slope to define the points where the pixel value needs to be changed.
However this does not work accurate since there are to many unpredictable scenarios.
The mask also exists in this format which is basically where i received the information from in this case all lines are in one array:
Related
I've trying to implement transition from an amount of space to another which is similar to acceleration and deceleration, except i failed and the only thing that i got from this was this infinite stack of mess, here is a screenshot showing this in action:
you can see a very black circle here, which are in reality something like 100 or 200 circles stacked on top of each other
and i reached this result using this piece of code:
def Place_circles(curve, circle_space, cs, draw=True, screen=None):
curve_acceleration = []
if type(curve) == tuple:
curve_acceleration = curve[1][0]
curve_intensity = curve[1][1]
curve = curve[0]
#print(curve_intensity)
#print(curve_acceleration)
Circle_list = []
idx = [0,0]
for c in reversed(range(0,len(curve))):
for p in reversed(range(0,len(curve[c]))):
user_dist = circle_space[curve_intensity[c]] + curve_acceleration[c] * p
dist = math.sqrt(math.pow(curve[c][p][0] - curve[idx[0]][idx[1]][0],2)+math.pow(curve [c][p][1] - curve[idx[0]][idx[1]][1],2))
if dist > user_dist:
idx = [c,p]
Circle_list.append(circles.circles(round(curve[c][p][0]), round(curve[c][p][1]), cs, draw, screen))
This place circles depending on the intensity (a number between 0 and 2, random) of the current curve, which equal to an amount of space (let's say between 20 and 30 here, 20 being index 0, 30 being index 2 and a number between these 2 being index 1).
This create the stack you see above and isn't what i want, i also came to the conclusion that i cannot use acceleration since the amount of time to move between 2 points depend on the amount of circles i need to click on, knowing that there are multiple circles between each points, but not being able to determine how many lead to me being unable to the the classic acceleration formula.
So I'm running out of options here and ideas on how to transition from an amount of space to another.
any idea?
PS: i scrapped the idea above and switched back to my master branch but the code for this is still available in the branch i created here https://github.com/Mrcubix/Osu-StreamGenerator/tree/acceleration .
So now I'm back with my normal code that don't possess acceleration or deceleration.
TL:DR i can't use acceleration since i don't know the amount of circles that are going to be placed between the 2 points and make the time of travel vary (i need for exemple to click circles at 180 bpm of one circle every 0.333s) so I'm looking for another way to generate gradually changing space.
First, i took my function that was generating the intensity for each curves in [0 ; 2]
Then i scrapped the acceleration formula as it's unusable.
Now i'm using a basic algorithm to determine the maximum amount of circles i can place on a curve.
Now the way my script work is the following:
i first generate a stream (multiple circles that need to be clicked at high bpm)
this way i obtain the length of each curves (or segments) of the polyline.
i generate an intensity for each curve using the following function:
def generate_intensity(Circle_list: list = None, circle_space: int = None, Args: list = None):
curve_intensity = []
if not Args or Args[0] == "NewProfile":
prompt = True
while prompt:
max_duration_intensity = input("Choose the maximum amount of curve the change in intensity will occur for: ")
if max_duration_intensity.isdigit():
max_duration_intensity = int(max_duration_intensity)
prompt = False
prompt = True
while prompt:
intensity_change_odds = input("Choose the odds of occurence for changes in intensity (1-100): ")
if intensity_change_odds.isdigit():
intensity_change_odds = int(intensity_change_odds)
if 0 < intensity_change_odds <= 100:
prompt = False
prompt = True
while prompt:
min_intensity = input("Choose the lowest amount of spacing a circle will have: ")
if min_intensity.isdigit():
min_intensity = float(min_intensity)
if min_intensity < circle_space:
prompt = False
prompt = True
while prompt:
max_intensity = input("Choose the highest amount of spacing a circle will have: ")
if max_intensity.isdigit():
max_intensity = float(max_intensity)
if max_intensity > circle_space:
prompt = False
prompt = True
if Args:
if Args[0] == "NewProfile":
return [max_duration_intensity, intensity_change_odds, min_intensity, max_intensity]
elif Args[0] == "GenMap":
max_duration_intensity = Args[1]
intensity_change_odds = Args[2]
min_intensity = Args[3]
max_intensity = Args[4]
circle_space = ([min_intensity, circle_space, max_intensity] if not Args else [Args[0][3],circle_space,Args[0][4]])
count = 0
for idx, i in enumerate(Circle_list):
if idx == len(Circle_list) - 1:
if random.randint(0,100) < intensity_change_odds:
if random.randint(0,100) > 50:
curve_intensity.append(2)
else:
curve_intensity.append(0)
else:
curve_intensity.append(1)
if random.randint(0,100) < intensity_change_odds:
if random.randint(0,100) > 50:
curve_intensity.append(2)
count += 1
else:
curve_intensity.append(0)
count += 1
else:
if curve_intensity:
if curve_intensity[-1] == 2 and not count+1 > max_duration_intensity:
curve_intensity.append(2)
count += 1
continue
elif curve_intensity[-1] == 0 and not count+1 > max_duration_intensity:
curve_intensity.append(0)
count += 1
continue
elif count+1 > 2:
curve_intensity.append(1)
count = 0
continue
else:
curve_intensity.append(1)
else:
curve_intensity.append(1)
curve_intensity.reverse()
if curve_intensity.count(curve_intensity[0]) == len(curve_intensity):
print("Intensity didn't change")
return circle_space[1]
print("\n")
return [circle_space, curve_intensity]
with this, i obtain 2 list, one with the spacing i specified, and the second one is the list of randomly generated intensity.
from there i call another function taking into argument the polyline, the previously specified spacings and the generated intensity:
def acceleration_algorithm(polyline, circle_space, curve_intensity):
new_circle_spacing = []
for idx in range(len(polyline)): #repeat 4 times
spacing = []
Length = 0
best_spacing = 0
for p_idx in range(len(polyline[idx])-1): #repeat 1000 times / p_idx in [0 ; 1000]
# Create multiple list containing spacing going from circle_space[curve_intensity[idx-1]] to circle_space[curve_intensity[idx]]
spacing.append(np.linspace(circle_space[curve_intensity[idx]],circle_space[curve_intensity[idx+1]], p_idx).tolist())
# Sum distance to find length of curve
Length += abs(math.sqrt((polyline[idx][p_idx+1][0] - polyline[idx][p_idx][0]) ** 2 + (polyline [idx][p_idx+1][1] - polyline[idx][p_idx][1]) ** 2))
for s in range(len(spacing)): # probably has 1000 list in 1 list
length_left = Length # Make sure to reset length for each iteration
for dist in spacing[s]: # substract the specified int in spacing[s]
length_left -= dist
if length_left > 0:
best_spacing = s
else: # Since length < 0, use previous working index (best_spacing), could also jsut do `s-1`
if spacing[best_spacing] == []:
new_circle_spacing.append([circle_space[1]])
continue
new_circle_spacing.append(spacing[best_spacing])
break
return new_circle_spacing
with this, i obtain a list with the space between each circles that are going to be placed,
from there, i can Call Place_circles() again, and obtain the new stream:
def Place_circles(polyline, circle_space, cs, DoDrawCircle=True, surface=None):
Circle_list = []
curve = []
next_circle_space = None
dist = 0
for c in reversed(range(0, len(polyline))):
curve = []
if type(circle_space) == list:
iter_circle_space = iter(circle_space[c])
next_circle_space = next(iter_circle_space, circle_space[c][-1])
for p in reversed(range(len(polyline[c])-1)):
dist += math.sqrt((polyline[c][p+1][0] - polyline[c][p][0]) ** 2 + (polyline [c][p+1][1] - polyline[c][p][1]) ** 2)
if dist > (circle_space if type(circle_space) == int else next_circle_space):
dist = 0
curve.append(circles.circles(round(polyline[c][p][0]), round(polyline[c][p][1]), cs, DoDrawCircle, surface))
if type(circle_space) == list:
next_circle_space = next(iter_circle_space, circle_space[c][-1])
Circle_list.append(curve)
return Circle_list
the result is a stream with varying space between circles (so accelerating or decelerating), the only issue left to be fixed is pygame not updating the screen with the new set of circle after i call Place_circles(), but that's an issue i'm either going to try to fix myself or ask in another post
the final code for this feature can be found on my repo : https://github.com/Mrcubix/Osu-StreamGenerator/tree/Acceleration_v02
So I'm working on a homework assignment regarding using image objects in python. I'm using python 3.4.1 for this assignment. I feel like I have everything done, but it doesn't want to work correctly. Basically, I'm trying to get it to look like the picture that I've attached, but it only shows as 1 red line across, and 1 red line top to bottom on a white background.
Any help would be much appreciated.
The attached image:
http://imgur.com/TMho41w
import cImage as image
width = 500
height = 500
img = image.EmptyImage(width, height)
win = image.ImageWin("Exercise 3", width, height)
img.draw(win)
for row in range(height):
for col in range(width):
p = img.getPixel(col, row)
if row == 0 or col == 0:
p = image.Pixel(255, 0, 0)
else:
Sum = 0
temppixel = img.getPixel(col-1, row)
if temppixel.getRed() == 255:
Sum = Sum + 1
elif temppixel.getBlue() == 255:
Sum = Sum + 2
temppixel = img.getPixel(col-1, row-1)
if temppixel.getRed() == 255:
Sum = Sum + 1
elif temppixel.getBlue() == 255:
Sum = Sum + 2
temppixel = img.getPixel(col, row-1)
if temppixel.getRed() == 255:
Sum = Sum + 1
elif temppixel.getBlue() == 255:
Sum = Sum + 2
if Sum % 3 == 1:
p = image.Pixel(255, 0, 0)
elif Sum % 3 == 2:
p = image.Pixel(0, 0, 255)
else:
p = image.Pixel(255, 255, 255)
img.setPixel(col, row, p)
img.draw(win)
img.draw(win)
# uncomment this to save the image as a file
#img.saveTk("gradient.gif")
win.exitonclick()
Unfortunately, your code does exactly what you have written it to do. Let's name the three first if ... elif condition1, 2 and 3 :
The first pixel is set to red
Then we progress through the first line, so row = 0 which means condition 2 and 3 are using invalid coordinates (because of row-1). So there's only condition at play here, and it will always increment by 1 sum which means it'll add a new red pixel.
So you have now your first red line.
For the first column, starting from the second line : conditions 1 & 2 are using invalid coordinates. Condition 3 will always return sum = 1 which means a new red pixel. And you have your red line from top to bottom
And then from row = 1 and col = 1, all neighbors are red, which leads to a new white pixel. Unfortunately, white does contain some red, so it'll always be the sames conditions that are met, and you have your white background.
I haven't been able to find the complete algorithm for this method to build a Sierpinski carpet, so I can't really correct it. But you should be extra careful with these edges situations : what should be the three neighbors if you are on the first line or first row ?
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)
I have been trying to write my own version of Conway's Game of Life as practice for Python using Pygame. I first wrote the functions for initializing the game field, and then calculating the next generation. I verified it's functionality using the console to print the results and verify that they returned the expected results (just 2 generations deep by hand on a 5x5 grid).
An important note of how I am calculating the neighbors... Instead of doing a for loop through the entire array and doing for loops to count for each cell, I have implemented an array that holds the neighbor counts. Only making changes when a cells status is changed. This means I don't waste time calculating neighbors for cells that have not changed.
When it came time to use Pygame to display the array with rectangles, I wrote the following program. At first I was drawing the screen by filling in the entire screen white, and then drawing the live cells as black (this can be done by commenting out the else statement in update()). I expected this to work as normal, but when I ran the program all I ended up with was the screen filling in black.
I was perplexed by the result so i drew white rectangles for the unpopulated cells (using the else statement. And got a better looking result, but instead of the cells eventually all dying, they eventually multiplied across the whole screen. This is opposite of what I expected, as I was expecting it to eventually stabilize.
Anyone know what I am doing wrong? I know that this is not the best way of writing this program, I welcome comments of how I can make it better.
RETURN = run simulation
'R' = randomize
'T' = tick one generation
'C' = clear game field
'N' = display neighbor map
import pygame
from pygame.locals import *
import numpy as np
from random import *
import copy
fieldSize = [100,50]
cellSize = 10 # size of >10 is recommended to see neighbor count
windowSize = [fieldSize[0]*cellSize, fieldSize[1]*cellSize]
# calculate the last cell in each axis so it is not done repeatedly
lastCell = [(fieldSize[0]-1), (fieldSize[1]-1)]
dX = float(windowSize[0])/float(fieldSize[0])
dY = float(windowSize[1])/float(fieldSize[1])
colorAlive = [0,125,0]
colorDead = [0, 0, 0]
# todo list
# 1. make cLife take in the field size
# 2. convert random functions to numpy.random.randint
class cLife():
def randomize(self):
self.neighbors = np.zeros(fieldSize)
# fill in the game field with random numbers
for x in range(fieldSize[0]):
for y in range(fieldSize[1]):
if(randint(0,99)<20):
self.gameField[x][y] = 1
self.updateNeighbors([x,y], True)
else:
self.gameField[x][y] = 0
def displayNeighbors(self, surface):
self.drawField(surface)
for x in range(fieldSize[0]):
for y in range(fieldSize[1]):
neighborCount=font.render(str(int(self.neighbors[x][y])), 1,(200,200,200))
surface.blit(neighborCount, (x*dX+dX/3, y*dY+dY/3.5))
pygame.display.flip()
# This is the function to update the neighbor map, the game field is torroidal so the complexity is greatly
# increased. I have handcoded each instruction to avoid countless if statements and for loops.
# Hopefully, this has drastically improved the performance. Using this method also allows me to avoid calculating
# the neighbor map for every single cell because the neighbor map is updated only for the cells affected by a change.
def updateNeighbors(self, pos, status):
if(status == True):
change = 1
else:
change = -1
# testing for the cells in the center of the field (most cells are in the center so this is first)
# cells are filled in starting on the top-left corner going clockwise
if((pos[0]>0 and pos[0]<lastCell[0])and(pos[1]>0 and pos[1]<lastCell[1])):
self.neighbors[pos[0]-1][pos[1]-1] += change
self.neighbors[pos[0]][pos[1]-1] += change
self.neighbors[pos[0]+1][pos[1]-1] += change
self.neighbors[pos[0]+1][pos[1]] += change
self.neighbors[pos[0]+1][pos[1]+1] += change
self.neighbors[pos[0]][pos[1]+1] += change
self.neighbors[pos[0]-1][pos[1]+1] += change
self.neighbors[pos[0]-1][pos[1]] += change
elif(pos[0] == 0): # left edge
if(pos[1] == 0): # top left corner
self.neighbors[lastCell[0]][lastCell[1]] += change
self.neighbors[0][lastCell[1]] += change
self.neighbors[1][lastCell[1]] += change
self.neighbors[1][0] += change
self.neighbors[1][1] += change
self.neighbors[0][1] += change
self.neighbors[lastCell[0]][1] += change
self.neighbors[lastCell[0]][0] += change
elif(pos[1] == lastCell[1]): # bottom left corner
self.neighbors[lastCell[0]][pos[1]-1] += change
self.neighbors[0][pos[1]-1] += change
self.neighbors[1][pos[1]-1] += change
self.neighbors[1][pos[1]] += change
self.neighbors[1][0] += change
self.neighbors[0][0] += change
self.neighbors[lastCell[0]][0] += change
self.neighbors[lastCell[0]][pos[1]] += change
else: # everything else
self.neighbors[lastCell[0]][pos[1]-1] += change
self.neighbors[0][pos[1]-1] += change
self.neighbors[1][pos[1]-1] += change
self.neighbors[1][pos[1]] += change
self.neighbors[1][pos[1]+1] += change
self.neighbors[0][pos[1]+1] += change
self.neighbors[lastCell[0]][pos[1]+1] += change
self.neighbors[lastCell[0]][pos[1]] += change
elif(pos[0] == lastCell[0]): # right edge
if(pos[1] == 0): # top right corner
self.neighbors[pos[0]-1][lastCell[1]] += change
self.neighbors[pos[0]][lastCell[1]] += change
self.neighbors[0][lastCell[1]] += change
self.neighbors[0][0] += change
self.neighbors[0][1] += change
self.neighbors[pos[0]][1] += change
self.neighbors[pos[0]-1][1] += change
self.neighbors[pos[0]-1][0] += change
elif(pos[1] == lastCell[1]): # bottom right corner
self.neighbors[pos[0]-1][pos[1]-1] += change
self.neighbors[pos[0]][pos[1]-1] += change
self.neighbors[0][pos[1]-1] += change
self.neighbors[0][pos[1]] += change
self.neighbors[0][0] += change
self.neighbors[pos[0]][0] += change
self.neighbors[pos[0]-1][0] += change
self.neighbors[pos[0]-1][pos[1]] += change
else: # everything else
self.neighbors[pos[0]-1][pos[1]-1] += change
self.neighbors[pos[0]][pos[1]-1] += change
self.neighbors[0][pos[1]-1] += change
self.neighbors[0][pos[1]] += change
self.neighbors[0][pos[1]+1] += change
self.neighbors[pos[0]][pos[1]+1] += change
self.neighbors[pos[0]-1][pos[1]+1] += change
self.neighbors[pos[0]-1][pos[1]] += change
elif(pos[1] == 0): # top edge, corners already taken care of
self.neighbors[pos[0]-1][lastCell[1]] += change
self.neighbors[pos[0]][lastCell[1]] += change
self.neighbors[pos[0]+1][lastCell[1]] += change
self.neighbors[pos[0]+1][0] += change
self.neighbors[pos[0]+1][1] += change
self.neighbors[pos[0]][1] += change
self.neighbors[pos[0]-1][1] += change
self.neighbors[pos[0]-1][0] += change
elif(pos[1] == lastCell[1]): # bottom edge, corners already taken care of
self.neighbors[pos[0]-1][pos[1]-1] += change
self.neighbors[pos[0]][pos[1]-1] += change
self.neighbors[pos[0]+1][pos[1]-1] += change
self.neighbors[pos[0]+1][pos[1]] += change
self.neighbors[pos[0]+1][0] += change
self.neighbors[pos[0]][0] += change
self.neighbors[pos[0]-1][0] += change
self.neighbors[pos[0]-1][pos[1]] += change
def nextGeneration(self):
# copy the neighbor map, because changes will be made during the update
self.neighborsOld = copy.deepcopy(self.neighbors)
for x in range(fieldSize[0]):
for y in range(fieldSize[1]):
# Any live cell with fewer than two live neighbours dies, as if caused by under-population.
if(self.gameField[x][y] == 1 and self.neighborsOld[x][y] < 2):
self.gameField[x][y] = 0
self.updateNeighbors([x,y], False)
# Any live cell with more than three live neighbours dies, as if by overcrowding.
elif(self.gameField[x][y] == 1 and self.neighborsOld[x][y] >3):
self.gameField[x][y] = 0
self.updateNeighbors([x,y], False)
# Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
elif(self.gameField[x][y] == 0 and self.neighborsOld[x][y] == 3):
self.gameField[x][y] = 1
self.updateNeighbors([x,y], True)
def drawField(self, surface):
surface.fill(colorDead)
# loop through and draw each live cell
for x in range(fieldSize[0]):
for y in range(fieldSize[1]):
if(self.gameField[x][y] == 1):
pygame.draw.rect(surface, colorAlive, [dX*x, dY*y, dX, dY])
pygame.display.flip()
def __init__(self):
# initialize the game field and neighbor map with zeros
self.gameField = np.zeros(fieldSize)
self.neighbors = np.zeros(fieldSize)
# begining of the program
game = cLife()
pygame.init()
surface = pygame.display.set_mode(windowSize)
pygame.display.set_caption("Conway\'s Game of Life")
clock = pygame.time.Clock()
pygame.font.init()
font=pygame.font.Font(None,10)
surface.fill(colorDead)
game.randomize()
game.drawField(surface)
pygame.display.flip()
running = False
while True:
#clock.tick(60)
# handling events
for event in pygame.event.get():
if(event.type == pygame.MOUSEBUTTONDOWN):
mousePos = pygame.mouse.get_pos()
x = int(mousePos[0]/dX)
y = int(mousePos[1]/dY)
if(game.gameField[x][y] == 0):
game.gameField[x][y] = 1
game.updateNeighbors([x, y], True)
game.drawField(surface)
else:
game.gameField[x][y] = 0
game.updateNeighbors([x, y], False)
game.drawField(surface)
elif(event.type == pygame.QUIT):
pygame.quit()
elif(event.type == pygame.KEYDOWN):
# return key starts and stops the simulation
if(event.key == pygame.K_RETURN):
if(running == False):
running = True
else:
running = False
# 't' key ticks the simulation forward one generation
elif(event.key == pygame.K_t and running == False):
game.nextGeneration()
game.drawField(surface)
# 'r' randomizes the playfield
elif(event.key == pygame.K_r):
game.randomize()
game.drawField(surface)
# 'c' clears the game field
elif(event.key == pygame.K_c):
running = False
game.gameField = np.zeros(fieldSize)
game.neighbors = np.zeros(fieldSize)
game.drawField(surface)
# 'n' displays the neighbor map
elif(event.key == pygame.K_n):
game.displayNeighbors(surface)
if(running == True):
game.nextGeneration()
game.drawField(surface)
self.neighborsOld = self.neighbors does not copy the map, it only points to it.
See :
a = [[1,2],[3,4]]
b = a
b[0][0] = 9
>>> a
[[9, 2], [3, 4]]
You need to either make a copy (a[:]) for every row in a, or use the copy module and use deepcopy:
b = [x[:] for x in a]
or
import copy
b = copy.deepcopy(a)
Either way, it results in
b[0][0] = 9
>>> a
[[1, 2], [3, 4]]
The code I'm writing puts up a shape on screen and allows for manipulation of the shape via the up/down arrow keys. What I've been trying to get it to do is make the amount the shape changes dependent on the sequence of keypresses; as long as the input is the same as the initial keypress, the amount the shape changes will be large. However, when the first 'reversal' of key press occurs (for instance, to make fine adjustments), every key press after that point (regardless if it is the same as the initial button press) should change the circle by a much smaller proportion (not interatively, just 0.1cm change rather than 2cm). The code has been written in Psychopy.
I think I've not grasped the way the loops should be set out for this, but I can't see how they might be changed to do what I want. Apologies for the actual code as opposed to a minimal example - any advice is much appreciated.
for thisTrial in trials:
endKey = 0
nKeypress = 0
count = 0
counting = 0
if thisTrial == 'ellipse':
ellipseHeightinit = 7.6,1.9 + (round(numpy.random.uniform(-1,1),1))
elif thisTrial == 'circle':
ellipseHeightinit = 7.6,7.6 + (round(numpy.random.uniform(-1,1),1))
ellipseHeight = ellipseHeightinit
ellipseStim.setSize(ellipseHeight, log = False) # set the initial size of the shape
while endKey == 0:
ellipseStim.setAutoDraw(True)
win.flip() # flip the window to see the stimuli
allKeys = event.waitKeys()
if count < 1: #store the first keypress made
for thisKey in allKeys:
firstKeypress = thisKey
count += 1
event.clearEvents()
for thisKey in allKeys: # change the size of the shape depending on key pressed
if thisKey == 'up':
nKeypress = nKeypress + 1
elif thisKey == 'down':
nKeypress = nKeypress - 1
elif thisKey == 'space':
endKey = 1
while counting < 1: # attempt to make step size large until reversal
if thisKey == firstKeypress:
ellipseHeight = 7.6, ellipseHeightinit[1] + nKeypress*20
break
elif thisKey != firstKeypress:
ellipseHeight = 7.6, ellipseHeightinit[1] + nKeypress*0.1
counting += 1
break
ellipseStim.setSize(ellipseHeight, log = False) # set new shape size
ellipseStim.setAutoDraw(False)
You should store somewhere which was the last key pressed, and which is the amount of movement last used. If the new key pressed is the same, you enlarge your amount. Otherwise you set it to the minimum value.