I'm completely new to programming and python as a whole but I have recently joined a class. Currently I am trying to make a program which prompts the user for an input of the coordinates of the center of an object on the screen.
The program will then take the X and Y coordinates of the item and randomize the coordinates within a range of +- 15. So if the X coordinate is 30, the program will pick a random number of 15 to 45.
Then the program will take a random delay between 3.2 to 4.3 seconds and move the mouse from it's current position to the randomized coordinates within the random time delay.
I want it to be able to loop endlessly until I prompt it to stop, and I think I can figure that out. However, I do not understand how to properly use parameters to allow the local variables coords_x , coords_y and click_delay to be used in the function main()
Here is what I have so far:
#! python3
import pyautogui, sys
import random
from random import randint
center_x = int(input("Please enter the X coordinate of the center of the item:"))
center_y = int(input("Please enter the Y coordinate of the center of the item:"))
#y = 23 long 735 - 712
#x = 23 862 - 838
buffer = 17
def randomizex():
min_x = center_x - buffer
max_x = center_x + buffer
coords_x = randint(min_x, max_x)
def randomizey():
min_y = center_y - buffer
max_y = center_y + buffer
coords_y = randint(min_y, max_y)
def randomizedelay():
click_delay = random.uniform(3.2, 4.3)
def main():
randomizex()
randomizey()
randomizedelay()
pyautogui.moveTo(coords_x, coords_y, click_delay)
main()
I appreciate any help. The other answers I have found to questions like these are rather confusing to a newbie and some of them are for python 2.
Welcome to SO (and to programming)!
You are almost there with this, the thing you are missing is saving the return values of the randomizex(), randomizey(), randomizedelay() functions to variables, so they can be used within main. Even though you name the variables within their respective functions, that naming does not go beyond the scope of these functions, so main has no idea they are called that. Something like this should work:
def main():
coords_x = randomizex()
coords_y = randomizey()
click_delay = randomizedelay()
pyautogui.moveTo(coords_x, coords_y, click_delay)
def main():
your_x_coords = randomizex()
your_y_coords = randomizey()
Your functions return x_coords, so you have to assign them to another local variable inside of main.
Related
I have a simpel Card Game, which I am currently working on for my thesis.
The Rules are simpel. You have a deck of 52 Cards, from 1 to 10 and jack, queen, knight.
You draw a card from your Deck. If its a Number it gets added to your Account. If you draw a jack, queen or knight, your account gets reset to 0. After every draw you can decide if you want to draw again or stop.
For this game, i programmed a code with the help of this site.
It should give the probability, that you draw exactly "target".
So for example, the probability to draw, so that you have 1 Point in your account,
is 4/52, since you have four 1´s. The Programm does give me exactly this value.
But. The probabiltity, that you have exactly 2 points in your account is
4/52 + 4/52*3/51. You can either draw a 2 with prob of 4/52 or a 1 and another 1 with prob 4/52*3/51.
Here the code messes up. It calculates the probability to have exactly 2 points in your account as
4/52 + 4/52*4/51 and i dont get why?
Can anyone help me?
import collections
import numpy as np
def probability(n, s, target):
prev = {0: 1} # previous roll is 0 for first time
for q in range(n):
cur = collections.defaultdict(int) # current probability
for r, times in prev.items():
cards = [card for card in range(1, 11)] * 4
for i in cards[:]:
cards.remove(i)
# if r occurred `times` times in the last iteration then
# r+i have `times` more possibilities for the current iteration.
cur[r + i] += times
prev = cur # use this for the next iteration
return (cur[t]*np.math.factorial(s-n)) / (np.math.factorial(s))
if __name__ == '__main__':
s = 52
for target in range(1, 151):
prob = 0
for n in range(1, 52):
prob += probability(n, s, target)
print(prob)
EDIT: I am fairly sure, that the line
for i in [i for i in cards]:
is the problem. Since cards.remove(i) removes the drawn card, but i doesnt care and can draw it anyway.
EDIT 2: Still searching. I tried the suggestions in this two qestions
How to remove list elements in a for loop in Python?
and
How to remove items from a list while iterating?
Nothing worked so far as it should.
I'm assuming with probability(n, s, target) you want to calculate the probability if you draw exactly n out of s cards that the sum of values is exactly target.
Then you will have a problem with n>=2. If I understand this right, for every iteration in the loop
for q in range(n):
you save in cur[sum] the number of ways to reach sum after drawing one card (p=0), two cards (p=1) and so on. But when you set p=1 you don't "remember" which card you have already drawn as you set
cards = [i for i in range(1, 11)] * 4
afterwards. So if you have drawn a "1" first (four possibilities) you have again still four "1"s you can draw out of your deck, which will give you your 4/52*4/51.
As a side note:
Shouldn't there be some kind of check if i==11 since that should reset your account?
I have solved it. After like a 4 Days.
This is the Code:
import numpy as np
def probability(cards, target, with_replacement = False):
x = 0 if with_replacement else 1
def _a(idx, l, r, t):
if t == sum(l):
r.append(l)
elif t < sum(l):
return
for u in range(idx, len(cards)):
_a(u + x, l + [cards[u]], r, t)
return r
return _a(0, [], [], target)
if __name__ == '__main__':
s = 52 # amount of cards in your deck
cards = [c for c in range(1, 11)] * 4
prob = 0
for target in range(1, 151): # run till 150 points
prob = probability(cards, target, with_replacement = False)
percentage = 0
for i in range(len(prob)):
percentage += np.math.factorial(len(prob[i])) * np.math.factorial(s-len(prob[i]))/(np.math.factorial(s))
print(percentage)
This Code is the Solution to my Question. Therefore this Thread can be closed.
For those who want to know, what it does as a tl;dr version.
You have a List (in this case Cards). The Code gives you every possible Combination of Elements in the List such as the Sum over the elements equals the target Value. Furthermore it also gives the Probability in the above mentioned Cardgame to draw a specific Value. The above mentioned game is basically the pig dice game but with cards.
I am working on a cost minimizing function to help with allocation/weights in a portfolio of stocks. I have the following code for the "Objective Function". This works when I tried it with 15 variables(stocks). However, when I tried it with 55 stocks it failed.
I have tried it with a smaller sample of stocks(15) and it works fine. The num_assets variable below is the number of stocks in the portfolio.
def get_metrics(weights):
weights = np.array(weights)
returnsR = np.dot(returns_annualR, weights )
volatilityR = np.sqrt(np.dot(weights.T, np.dot(cov_matrixR, weights)))
sharpeR = returnsR / volatilityR
drawdownR = np.multiply(weights, dailyDD).sum(axis=1, skipna =
True).min()
drawdownR = f(drawdownR)
calmarR = returnsR / drawdownR
results = (sharpeR * 0.3) + (calmarR * 0.7)
return np.array([returnsR, volatilityR, sharpeR, drawdownR, calmarR,
results])
def objective(weights):
# the number 5 is the index from the get_metrics array
return get_metrics(weights)[5] * -1
def check_sum(weights):
#return 0 if sum of the weights is 1
return np.sum(weights)-1
bound = (0.0,1.0)
bnds = tuple(bound for x in range (num_assets))
bx = list(bnds)
""" Custom step-function """
class RandomDisplacementBounds(object):
"""random displacement with bounds: see: https://stackoverflow.com/a/21967888/2320035
Modified! (dropped acceptance-rejection sampling for a more specialized approach)
"""
def __init__(self, xmin, xmax, stepsize=0.5):
self.xmin = xmin
self.xmax = xmax
self.stepsize = stepsize
def __call__(self, x):
"""take a random step but ensure the new position is within the bounds """
min_step = np.maximum(self.xmin - x, -self.stepsize)
max_step = np.minimum(self.xmax - x, self.stepsize)
random_step = np.random.uniform(low=min_step, high=max_step, size=x.shape)
xnew = x + random_step
return xnew
bounded_step = RandomDisplacementBounds(np.array([b[0] for b in bx]), np.array([b[1] for b in bx]))
minimizer_kwargs = {"method":"L-BFGS-B", "bounds": bnds}
globmin = sco.basinhopping(objective,
x0=num_assets*[1./num_assets],
minimizer_kwargs=minimizer_kwargs,
take_step=bounded_step,
disp=True)
The output should be an array of numbers that add up to 1 or 100%. However, this is not happening.
This function is a failure on my end as well. It failed to choose values which were lower -- ie., regardless of output from optimization function (negative or positive), it persisted until the parameter I was optimizing was as bad as it could possibly be. I suspect that since the function violates function encapsulation and relies on "function attributes" to adjust stepsize, the developer may not have respected encapsulated function scope elsewhere, and surprising behavior is happening as a result.
Regardless, in terms of theory, anything else is just a (dubious) estimated numerical partial second derivative (numerical Hessian, or "estimated curvature" for us mere mortals) based "performance" "gain", which reduces to a randomly-biased annealer in discrete, chaotic (phase space, continuous) or mixed (continuous and discrete) search spaces with volatile curvatures or planar areas (due to numerical underflow and loss of precision).
Anyways, import the following:
scipy.optimize.dual_anneal
dual anneal
I'm pretty new to python and Opencv, but I have a few pieces from cv2 and random in mind for a simple test program to make sure I understood how these libraries worked.
I'm trying to create a program that effectively generates colored "snow", similar to what an old fashioned television shows when it has no signal.
Basically I generate a random color with random.randint(-1,256) to get a value between 0 and 255. I do it three times and store each in a different variable, randB/G/R. Then I do it twice more for coordinates randX/Y, using img.shape to get variables for width and height for the max number.
I don't think my variables are being interpreted as strings. If I quickly break the loop and print my variables, no errors are shown. If I remove the randX and randY variables and specify fixed coordinates or a range of [X1:Y1, X2:Y2] it doesn't crash.
import cv2
import numpy as np
import random
img = cv2.imread('jake_twitch.png', cv2.IMREAD_COLOR)
height, width, channels = img.shape
while True:
randB = (random.randint(-1,256))
randG = (random.randint(-1,256))
randR = (random.randint(0,256))
randX = (random.randint(0,width))
randY = (random.randint(0,height))
img[randX,randY] = [randB,randG,randR]
cv2.imshow('Snow', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.imwrite('Snow.png', img)
cv2.destroyAllWindows
I would expect my code to run indefinitely coloring pixels random colors within a specified "box" defined by the width and height variables from img.shape.
it seems to start doing that, but If the program runs for more than about a second it crashes and spits out this error
"IndexError: index 702 is out of bounds for axis 1 with size 702"
Your image is width and height pixels wide - but the corresponding indexes run from 0..width-1 and 0..height-1
The randint function returns inclusive limits - so
random.randint(0,width)
might give you width ... which is 1 too big:
random.randint(a, b)
Return a random integer N such that a <= N <= b. Alias for randrange(a, b+1).
Use
randX = (random.randint(0,width-1))
randY = (random.randint(0,height-1))
instead.
Or change it to use random.randrange(0, width) or random.choice(range(width)) - both omit the upper limit value.
I have been using Python to control some instruments, which I created a Class for. I have multiple instruments of the same kind, so my script has multiple instances of the same class.
Let's say the class is Arm, and it has methods move_left, move_right and reset. Right now I have script like this:
arm1 = Arm()
arm2 = Arm()
arm1.move_left()
arm2.move_left()
arm1.move_right()
arm2.move_right()
arm1.reset()
arm2.reset()
It's completely in serial. I have to wait for arm1 to finish move_left, then start arm2 to move_left. This is very inefficient. I would like arm1 and arm2 to move at the same time. They don't have to be exact same time, because arm1 and arm2 are quite independent and there's not much synchronization requirement. I just don't want to waste time in the serialization in the code.
I've done some searching and learned a little about threading, but what I found is all about putting a function in a Thread target, which doesn't really apply to my situation here.
One way to approach the problem would be to implement a state machine. That is, instead of defining the problem through commands like move_left() and move_right(), instead you can have some variables that represent the final position that you want each arm to end up at, and a second set of variables that represent the current position of the arm. Then at each time-step, you simply move the arms by a small amount towards their target-destination.
Here's a very simple toy program to demonstrate the idea. Note that it moves each "arm" by no more than 0.1 units every 100mS time-step (you can of course use any time-step and maximum-movement values you want instead):
import time
class Robot:
def __init__(self):
self._leftArmCurrentPos = 0.0
self._leftArmTargetPos = 0.0
self._rightArmCurrentPos = 0.0
self._rightArmTargetPos = 0.0
def setLeftArmTargetPos(self, newPos):
self._leftArmTargetPos = newPos
def setRightArmTargetPos(self, newPos):
self._rightArmTargetPos = newPos
# Returns the closest value to (deltaVal) in the range [-0.1, +0.1]
def clamp(self, deltaVal):
aLittleBit = 0.1 # or however much you want
if (deltaVal > aLittleBit):
return aLittleBit
elif (deltaVal < -aLittleBit):
return -aLittleBit
else:
return deltaVal
def moveArmsTowardsTargetPositions(self):
leftArmDelta = self.clamp(self._leftArmTargetPos - self._leftArmCurrentPos)
if (leftArmDelta != 0.0):
self._leftArmCurrentPos += leftArmDelta
print("Moved left arm by %f towards %f, new left arm pos is %f" % (leftArmDelta, self._leftArmTargetPos, self._leftArmCurrentPos))
rightArmDelta = self.clamp(self._rightArmTargetPos - self._rightArmCurrentPos)
if (rightArmDelta != 0.0):
self._rightArmCurrentPos += rightArmDelta
print("Moved right arm by %f towards %f, new right arm pos is %f" % (rightArmDelta, self._rightArmTargetPos, self._rightArmCurrentPos))
if __name__ == "__main__":
r = Robot()
r.setLeftArmTargetPos(10.0)
r.setRightArmTargetPos(-3.0)
while True:
r.moveArmsTowardsTargetPositions()
time.sleep(0.1)
A nice side-effect of this approach is that you if change your mind at any time about where you want the arms to be, you can simply call setLeftArmTargetPos() or setRightArmTargetPos() to give the arms new/different destination values, and they will immediately start moving from (wherever they currently are at) towards the new target positions -- there's no need to wait for them to arrive at the old destinations first.
I'm writing a script to chroma key (green screen) and composite some videos using Python and PIL (pillow). I can key the 720p images, but there's some left over green spill. Understandable but I'm writing a routine to remove that spill...however I'm struggling with how long it's taking. I can probably get better speeds using numpy tricks, but I'm not that familiar with it. Any ideas?
Here's my despill routine. It takes a PIL image and a sensitivity number but I've been leaving that at 1 so far...it's been working well. I'm coming in at just over 4 seconds for a 720p frame to remove this spill. For comparison, the chroma key routine runs in about 2 seconds per frame.
def despill(img, sensitivity=1):
"""
Blue limits green.
"""
start = time.time()
print '\t[*] Starting despill'
width, height = img.size
num_channels = len(img.getbands())
out = Image.new("RGBA", img.size, color=0)
for j in range(height):
for i in range(width):
#r,g,b,a = data[j,i]
r,g,b,a = img.getpixel((i,j))
if g > (b*sensitivity):
out_g = (b*sensitivity)
else:
out_g = g
# end if
out.putpixel((i,j), (r,out_g,b,a))
# end for
# end for
out.show()
print '\t[+] done.'
print '\t[!] Took: %0.1f seconds' % (time.time()-start)
exit()
return out
# end despill
Instead of putpixel, I tried to write the output pixel values to a numpy array then convert the array to a PIL image, but that was averaging just over 5 seconds...so this was faster somehow. I know putpixel isn't the snappiest option but I'm at a loss...
putpixel is slow, and loops like that are even slower, since they are run by the Python interpreter, which is slow as hell. The usual solution is to convert immediately the image to a numpy array and solve the problem with vectorized operations on it, which run in heavily optimized C code. In your case I would do something like:
arr = np.array(img)
g = arr[:,:,1]
bs = arr[:,:,2]*sensitivity
cond = g>bs
arr[:,:,1] = cond*bs + (~cond)*g
out = Image.fromarray(arr)
(it may not be correct and I'm sure it can be optimized way better, this is just a sketch)