import time #runs scored per game rsg = [4.26,4.75,4.19,4.52,4.28,4.36,4.41,4.39,4.54,3.24,4.49,3.82,3.92,5.19,3.99,4.67,4.56,4.66, 5.34,3.44,4.63,3.57,4.37,4.06,4.62,4.61,4.13,4.55,4.86,3.85] #runs allowed per game rag = [4.6,3.98,4.25,4.74,4.94,4.58,5.33,4.26,5.31,4.4,3.37,5.06,4.28,3.28,4.27,4.23,4.29,3.74,3.29, 4.5,4.12,5.04,4.07,3.85,4.36,4,3.93,4.47,4.29,5.45] #is you winner or loser? #I have included .500 teams as winner (you're welcome Chicago White Sox) #1 means your team is losing (should be above the line), 0 means your team is winning (below the line) w = [1,0,0,1,1,0,1,0,1,1,0,1,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1] teams = ['Arizona Diamondbacks','Atlanta Braves','Baltimore Orioles','Boston Red Sox','Chicago Cubs', 'Chicago White Sox','Cincinnati Reds','Cleveland Guardians','Colorado Rockies','Detroit Tigers', 'Houston Astros','Kansas City Royals','Los Angeles Angels','Los Angeles Dodgers','Miami Marlins', 'Milwaukee Brewers','Minnesota Twins','New York Mets','New York Yankees','Oakland Athletics', 'Philadelphia Phillies','Pittsburgh Pirates','San Diego Padres','Seattle Mariners', 'San Francisco Giants','St. Louis Cardinals','Tampa Bay Rays','Texas Rangers','Toronto Blue Jays', 'Washington Nationals'] #winner runs scored winner_rsg = [] #loser runs scored loser_rsg = [] #winner runs allowed winner_rag = [] #loser runs allowed loser_rag = [] #now separate all the data into 2 sets: winners and losers for i in range(len(rsg)): if w[i] > 0: loser_rsg += [rsg[i]] loser_rag += [rag[i]] else: winner_rsg += [rsg[i]] winner_rag += [rag[i]] import matplotlib.pyplot as plt speed = 0.5 #how many seconds in between graphs #draw a scatter plot of actual winners and losers as of 7/31/2022 def draw_scatter(): for i, label in enumerate(teams): plt.annotate(label, (rsg[i]+0.02, rag[i]+0.02)) plt.title("Separating Winning Teams By Runs Allowed and Scored") plt.xlabel("Runs Scored/Game") plt.ylabel("Runs Allowed/Game") plt.scatter(winner_rsg, winner_rag) plt.scatter(loser_rsg, loser_rag) plt.legend(['Winning Teams','Losing Teams']) plt.ion() draw_scatter() x = list(range(30,55,1)) x = [data/10 for data in x] #starting coefficients #coefficients = [w0, w1, w2] #you can basically start these coefficients with any random numbers because you haven't even looked a team yet coeff = [0, 1, 0.5] #now we will graph the first hypothesized line based on just a random guess y = [(coeff[1]*data -1*coeff[0])/coeff[2] for data in x] plt.plot(x,y) #use a line graph plt.draw() #draw the graph plt.pause(speed) #wait 1 second plt.clf() #clear the graph n = 0.2 #this number determins how much the graph changes when you find a new point to classify #now we are going to go through the real data points for all 30 of the teams for i in range(len(rsg)): pointvalue = coeff[1]*rag[i] + coeff[2]*rsg[i] + coeff[0] * 1 #these are the 2 options for properly classified points, so d = 1 if (w[i] == 1 and pointvalue > 1) or (w[i] == 0 and pointvalue < 1): d = 1 #these are the 2 options for misclassified points, so d = -1 if (w[i] == 0 and pointvalue > 1) or (w[i] == 1 and pointvalue < 1): d = -1 #if d=-1, we have found a misclassified point, so we will update the list of coefficients for the new point if d == -1: coeff[0] = coeff[0] + n * d * 1 coeff[1] = coeff[1] + n * d * rag[i] coeff[2] = coeff[2] + n * d * rsg[i] #now replot the dividing line y = [(coeff[1]*data -1*coeff[0])/coeff[2] for data in x] draw_scatter() plt.plot(x,y) plt.draw() plt.pause(speed) #only clear if you aren't on the final data point if i != len(rsg)-1: plt.clf() print(coeff)
Category Archives: Python
Minmax Tic Tac Toe
#!/usr/bin/env python3 from math import inf as infinity from random import choice import platform import time from os import system HUMAN = -1 COMP = +1 board = [ [0, 0, 0], [0, 0, 0], [0, 0, 0], ] def evaluate(state): """ Function to heuristic evaluation of state. :param state: the state of the current board :return: +1 if the computer wins; -1 if the human wins; 0 draw """ if wins(state, COMP): score = +1 elif wins(state, HUMAN): score = -1 else: score = 0 return score def wins(state, player): """ This function tests if a specific player wins. Possibilities: * Three rows [X X X] or [O O O] * Three cols [X X X] or [O O O] * Two diagonals [X X X] or [O O O] :param state: the state of the current board :param player: a human or a computer :return: True if the player wins """ win_state = [ [state[0][0], state[0][1], state[0][2]], [state[1][0], state[1][1], state[1][2]], [state[2][0], state[2][1], state[2][2]], [state[0][0], state[1][0], state[2][0]], [state[0][1], state[1][1], state[2][1]], [state[0][2], state[1][2], state[2][2]], [state[0][0], state[1][1], state[2][2]], [state[2][0], state[1][1], state[0][2]], ] if [player, player, player] in win_state: return True else: return False def game_over(state): """ This function test if the human or computer wins :param state: the state of the current board :return: True if the human or computer wins """ return wins(state, HUMAN) or wins(state, COMP) def empty_cells(state): """ Each empty cell will be added into cells' list :param state: the state of the current board :return: a list of empty cells """ cells = [] for x, row in enumerate(state): for y, cell in enumerate(row): if cell == 0: cells.append([x, y]) return cells def valid_move(x, y): """ A move is valid if the chosen cell is empty :param x: X coordinate :param y: Y coordinate :return: True if the board[x][y] is empty """ if [x, y] in empty_cells(board): return True else: return False def set_move(x, y, player): """ Set the move on board, if the coordinates are valid :param x: X coordinate :param y: Y coordinate :param player: the current player """ if valid_move(x, y): board[x][y] = player return True else: return False def minimax(state, depth, player): """ AI function that choice the best move :param state: current state of the board :param depth: node index in the tree (0 <= depth <= 9), but never nine in this case (see iaturn() function) :param player: an human or a computer :return: a list with [the best row, best col, best score] """ if player == COMP: best = [-1, -1, -infinity] else: best = [-1, -1, +infinity] if depth == 0 or game_over(state): score = evaluate(state) return [-1, -1, score] for cell in empty_cells(state): x, y = cell[0], cell[1] state[x][y] = player score = minimax(state, depth - 1, -player) state[x][y] = 0 score[0], score[1] = x, y if player == COMP: if score[2] > best[2]: best = score # max value else: if score[2] < best[2]: best = score # min value return best def clean(): """ Clears the console """ os_name = platform.system().lower() if 'windows' in os_name: system('cls') else: system('clear') def render(state, c_choice, h_choice): """ Print the board on console :param state: current state of the board """ chars = { -1: h_choice, +1: c_choice, 0: ' ' } str_line = '---------------' print('\n' + str_line) for row in state: for cell in row: symbol = chars[cell] print(f'| {symbol} |', end='') print('\n' + str_line) def ai_turn(c_choice, h_choice): """ It calls the minimax function if the depth < 9, else it choices a random coordinate. :param c_choice: computer's choice X or O :param h_choice: human's choice X or O :return: """ depth = len(empty_cells(board)) if depth == 0 or game_over(board): return clean() print(f'Computer turn [{c_choice}]') render(board, c_choice, h_choice) if depth == 9: x = choice([0, 1, 2]) y = choice([0, 1, 2]) else: move = minimax(board, depth, COMP) x, y = move[0], move[1] set_move(x, y, COMP) time.sleep(1) def human_turn(c_choice, h_choice): """ The Human plays choosing a valid move. :param c_choice: computer's choice X or O :param h_choice: human's choice X or O :return: """ depth = len(empty_cells(board)) if depth == 0 or game_over(board): return # Dictionary of valid moves move = -1 moves = { 1: [0, 0], 2: [0, 1], 3: [0, 2], 4: [1, 0], 5: [1, 1], 6: [1, 2], 7: [2, 0], 8: [2, 1], 9: [2, 2], } clean() print(f'Human turn [{h_choice}]') render(board, c_choice, h_choice) while move < 1 or move > 9: try: move = int(input('Use numpad (1..9): ')) coord = moves[move] can_move = set_move(coord[0], coord[1], HUMAN) if not can_move: print('Bad move') move = -1 except (EOFError, KeyboardInterrupt): print('Bye') exit() except (KeyError, ValueError): print('Bad choice') def main(): """ Main function that calls all functions """ clean() h_choice = '' # X or O c_choice = '' # X or O first = '' # if human is the first # Human chooses X or O to play while h_choice != 'O' and h_choice != 'X': try: print('') h_choice = input('Choose X or O\nChosen: ').upper() except (EOFError, KeyboardInterrupt): print('Bye') exit() except (KeyError, ValueError): print('Bad choice') # Setting computer's choice if h_choice == 'X': c_choice = 'O' else: c_choice = 'X' # Human may starts first clean() while first != 'Y' and first != 'N': try: first = input('First to start?[y/n]: ').upper() except (EOFError, KeyboardInterrupt): print('Bye') exit() except (KeyError, ValueError): print('Bad choice') # Main loop of this game while len(empty_cells(board)) > 0 and not game_over(board): if first == 'N': ai_turn(c_choice, h_choice) first = '' human_turn(c_choice, h_choice) ai_turn(c_choice, h_choice) # Game over message if wins(board, HUMAN): clean() print(f'Human turn [{h_choice}]') render(board, c_choice, h_choice) print('YOU WIN!') elif wins(board, COMP): clean() print(f'Computer turn [{c_choice}]') render(board, c_choice, h_choice) print('YOU LOSE!') else: clean() render(board, c_choice, h_choice) print('DRAW!') exit() main()
Naive Learning
import itertools import time import numpy as np import cv2 from moviepy.editor import VideoClip WORLD_HEIGHT = 4 WORLD_WIDTH = 4 WALL_FRAC = .2 NUM_WINS = 5 NUM_LOSE = 10 class GridWorld: def __init__(self, world_height=3, world_width=4, discount_factor=.5, default_reward=-.5, wall_penalty=-.6, win_reward=5., lose_reward=-10., viz=True, patch_side=120, grid_thickness=2, arrow_thickness=3, wall_locs=[[1, 1], [1, 2]], win_locs=[[0, 3]], lose_locs=[[1, 3]], start_loc=[0, 0], reset_prob=.2): self.world = np.ones([world_height, world_width]) * default_reward self.reset_prob = reset_prob self.world_height = world_height self.world_width = world_width self.wall_penalty = wall_penalty self.win_reward = win_reward self.lose_reward = lose_reward self.default_reward = default_reward self.discount_factor = discount_factor self.patch_side = patch_side self.grid_thickness = grid_thickness self.arrow_thickness = arrow_thickness self.wall_locs = np.array(wall_locs) self.win_locs = np.array(win_locs) self.lose_locs = np.array(lose_locs) self.at_terminal_state = False self.auto_reset = True self.random_respawn = True self.step = 0 self.viz_canvas = None self.viz = viz self.path_color = (128, 128, 128) self.wall_color = (0, 255, 0) self.win_color = (0, 0, 255) self.lose_color = (255, 0, 0) self.world[self.wall_locs[:, 0], self.wall_locs[:, 1]] = self.wall_penalty self.world[self.lose_locs[:, 0], self.lose_locs[:, 1]] = self.lose_reward self.world[self.win_locs[:, 0], self.win_locs[:, 1]] = self.win_reward spawn_condn = lambda loc: self.world[loc[0], loc[1]] == self.default_reward self.spawn_locs = np.array([loc for loc in itertools.product(np.arange(self.world_height), np.arange(self.world_width)) if spawn_condn(loc)]) self.start_state = np.array(start_loc) self.bot_rc = None self.reset() self.actions = [self.up, self.left, self.right, self.down, self.noop] self.action_labels = ['UP', 'LEFT', 'RIGHT', 'DOWN', 'NOOP'] self.q_values = np.ones([self.world.shape[0], self.world.shape[1], len(self.actions)]) * 1. / len(self.actions) if self.viz: self.init_grid_canvas() self.video_out_fpath = 'shm_dqn_gridsolver-' + str(time.time()) + '.mp4' self.clip = VideoClip(self.make_frame, duration=15) def make_frame(self, t): self.action() frame = self.highlight_loc(self.viz_canvas, self.bot_rc[0], self.bot_rc[1]) return frame def check_terminal_state(self): if self.world[self.bot_rc[0], self.bot_rc[1]] == self.lose_reward \ or self.world[self.bot_rc[0], self.bot_rc[1]] == self.win_reward: self.at_terminal_state = True # print('------++++---- TERMINAL STATE ------++++----') # if self.world[self.bot_rc[0], self.bot_rc[1]] == self.win_reward: # print('GAME WON! :D') # elif self.world[self.bot_rc[0], self.bot_rc[1]] == self.lose_reward: # print('GAME LOST! :(') if self.auto_reset: self.reset() def reset(self): # print('Resetting') if not self.random_respawn: self.bot_rc = self.start_state.copy() else: self.bot_rc = self.spawn_locs[np.random.choice(np.arange(len(self.spawn_locs)))].copy() self.at_terminal_state = False def up(self): action_idx = 0 # print(self.action_labels[action_idx]) new_r = self.bot_rc[0] - 1 if new_r < 0 or self.world[new_r, self.bot_rc[1]] == self.wall_penalty: return self.wall_penalty, action_idx self.bot_rc[0] = new_r reward = self.world[self.bot_rc[0], self.bot_rc[1]] self.check_terminal_state() return reward, action_idx def left(self): action_idx = 1 # print(self.action_labels[action_idx]) new_c = self.bot_rc[1] - 1 if new_c < 0 or self.world[self.bot_rc[0], new_c] == self.wall_penalty: return self.wall_penalty, action_idx self.bot_rc[1] = new_c reward = self.world[self.bot_rc[0], self.bot_rc[1]] self.check_terminal_state() return reward, action_idx def right(self): action_idx = 2 # print(self.action_labels[action_idx]) new_c = self.bot_rc[1] + 1 if new_c >= self.world.shape[1] or self.world[self.bot_rc[0], new_c] == self.wall_penalty: return self.wall_penalty, action_idx self.bot_rc[1] = new_c reward = self.world[self.bot_rc[0], self.bot_rc[1]] self.check_terminal_state() return reward, action_idx def down(self): action_idx = 3 # print(self.action_labels[action_idx]) new_r = self.bot_rc[0] + 1 if new_r >= self.world.shape[0] or self.world[new_r, self.bot_rc[1]] == self.wall_penalty: return self.wall_penalty, action_idx self.bot_rc[0] = new_r reward = self.world[self.bot_rc[0], self.bot_rc[1]] self.check_terminal_state() return reward, action_idx def noop(self): action_idx = 4 # print(self.action_labels[action_idx]) reward = self.world[self.bot_rc[0], self.bot_rc[1]] self.check_terminal_state() return reward, action_idx def qvals2probs(self, q_vals, epsilon=1e-4): action_probs = q_vals - q_vals.min() + epsilon action_probs = action_probs / action_probs.sum() return action_probs def action(self): # print('================ ACTION =================') if self.at_terminal_state: print('At terminal state, please call reset()') exit() # print('Start position:', self.bot_rc) start_bot_rc = self.bot_rc[0], self.bot_rc[1] q_vals = self.q_values[self.bot_rc[0], self.bot_rc[1]] action_probs = self.qvals2probs(q_vals) reward, action_idx = np.random.choice(self.actions, p=action_probs)() # print('End position:', self.bot_rc) # print('Reward:', reward) alpha = np.exp(-self.step / 10e9) self.step += 1 qv = (1 - alpha) * q_vals[action_idx] + alpha * (reward + self.discount_factor * self.q_values[self.bot_rc[0], self.bot_rc[1]].max()) self.q_values[start_bot_rc[0], start_bot_rc[1], action_idx] = qv if self.viz: self.update_viz(start_bot_rc[0], start_bot_rc[1]) if np.random.rand() < self.reset_prob: # print('-----> Randomly resetting to a random spawn point with probability', self.reset_prob) self.reset() def highlight_loc(self, viz_in, i, j): starty = i * (self.patch_side + self.grid_thickness) endy = starty + self.patch_side startx = j * (self.patch_side + self.grid_thickness) endx = startx + self.patch_side viz = viz_in.copy() cv2.rectangle(viz, (startx, starty), (endx, endy), (255, 255, 255), thickness=self.grid_thickness) return viz def update_viz(self, i, j): starty = i * (self.patch_side + self.grid_thickness) endy = starty + self.patch_side startx = j * (self.patch_side + self.grid_thickness) endx = startx + self.patch_side patch = np.zeros([self.patch_side, self.patch_side, 3]).astype(np.uint8) if self.world[i, j] == self.default_reward: patch[:, :, :] = self.path_color elif self.world[i, j] == self.wall_penalty: patch[:, :, :] = self.wall_color elif self.world[i, j] == self.win_reward: patch[:, :, :] = self.win_color elif self.world[i, j] == self.lose_reward: patch[:, :, :] = self.lose_color if self.world[i, j] == self.default_reward: action_probs = self.qvals2probs(self.q_values[i, j]) x_component = action_probs[2] - action_probs[1] y_component = action_probs[0] - action_probs[3] magnitude = 1. - action_probs[-1] s = self.patch_side // 2 x_patch = int(s * x_component) y_patch = int(s * y_component) arrow_canvas = np.zeros_like(patch) vx = s + x_patch vy = s - y_patch cv2.arrowedLine(arrow_canvas, (s, s), (vx, vy), (255, 255, 255), thickness=self.arrow_thickness, tipLength=0.5) gridbox = (magnitude * arrow_canvas + (1 - magnitude) * patch).astype(np.uint8) self.viz_canvas[starty:endy, startx:endx] = gridbox else: self.viz_canvas[starty:endy, startx:endx] = patch def init_grid_canvas(self): org_h, org_w = self.world_height, self.world_width viz_w = (self.patch_side * org_w) + (self.grid_thickness * (org_w - 1)) viz_h = (self.patch_side * org_h) + (self.grid_thickness * (org_h - 1)) self.viz_canvas = np.zeros([viz_h, viz_w, 3]).astype(np.uint8) for i in range(org_h): for j in range(org_w): self.update_viz(i, j) def solve(self): if not self.viz: while True: self.action() else: self.clip.write_videofile(self.video_out_fpath, fps=460) def gen_world_config(h, w, wall_frac=.5, num_wins=2, num_lose=3): n = h * w num_wall_blocks = int(wall_frac * n) wall_locs = (np.random.rand(num_wall_blocks, 2) * [h, w]).astype(np.int) win_locs = (np.random.rand(num_wins, 2) * [h, w]).astype(np.int) lose_locs = (np.random.rand(num_lose, 2) * [h, w]).astype(np.int) return wall_locs, win_locs, lose_locs if __name__ == '__main__': wall_locs, win_locs, lose_locs = gen_world_config(WORLD_HEIGHT, WORLD_WIDTH, WALL_FRAC, NUM_WINS, NUM_LOSE) g = GridWorld(world_height=WORLD_HEIGHT, world_width=WORLD_WIDTH, wall_locs=wall_locs, win_locs=win_locs, lose_locs=lose_locs, viz=True) g.solve() k = 0
Calculating the nth Digit of Pi
Pygame Sprite Motion with Wrap-Around
import pygame WIDTH = 500 HEIGHT = 500 FPS = 60 #define colors #colors are defined in red,green,blue #values from 0-255 WHITE = (255,255,255) BLACK = (0,0,0) RED = (255,0,0) GREEN = (0,255,0) BLUE = (0,0,255) YELLOW = (255,255,0) pygame.init() pygame.mixer.init() screen = pygame.display.set_mode((WIDTH,HEIGHT)) clock = pygame.time.Clock() class Player(pygame.sprite.Sprite): def __init__(self): pygame.sprite.Sprite.__init__(self) self.image = pygame.Surface((50,40)) self.image.fill(GREEN) self.rect = self.image.get_rect() self.rect.centerx = 30 self.rect.centery = 30 self.speedx = 0 self.speedy = 0 self.ticker = 0 def update(self): speed = 5 self.speedx = 0 self.speedy = 0 keystate = pygame.key.get_pressed() if keystate[pygame.K_LEFT]: self.speedx = -speed if keystate[pygame.K_RIGHT]: self.speedx = speed if keystate[pygame.K_UP]: self.speedy = -speed self.ticker += 1 #NEW CODE if keystate[pygame.K_DOWN]: self.speedy = speed self.ticker += 1 #NEW CODE self.rect.x += self.speedx #HERE IS WHERE WE self.ticker = self.ticker % 20 if self.ticker >9: self.image.fill(GREEN) else: self.image.fill(BLUE) #HERE WE ARE GOING TO WRAP AROUND THE EDGES IF THE SPRITE #GOES OFF THE SCREEN #REMEMBER THAT X IS LEFT TO RIGHT AND Y IS UP AND DOWN if self.rect.x > WIDTH: self.rect.x = 0 if self.rect.x < 0: self.rect.x = WIDTH self.rect.y += self.speedy if self.rect.y > HEIGHT: self.rect.y = 0 if self.rect.y < 0: self.rect.y = HEIGHT all_sprites = pygame.sprite.Group() james = Player() all_sprites.add(james) running = True while running: clock.tick(FPS) pygame.event.get() all_sprites.update() screen.fill(BLACK) all_sprites.draw(screen) pygame.display.flip()
Python Brickbreaker with Tkinter
from tkinter import * import random import time tk = Tk() tk.title("Game") tk.resizable(0, 0) tk.wm_attributes("-topmost", 1) canvas = Canvas(tk, width=500, height=400, bd=0, highlightthickness=0) canvas.pack() tk.update() class Ball: def __init__(self, canvas, paddle, color): self.canvas = canvas self.paddle = paddle self.id = canvas.create_oval(10, 10, 25, 25, fill=color) self.canvas.move(self.id, 245, 100) starts = [-3, -2, -1, 1, 2, 3] random.shuffle(starts) self.x = starts[0] self.y = -3 self.canvas_height = self.canvas.winfo_height() self.canvas_width = self.canvas.winfo_width() def draw(self): self.canvas.move(self.id, self.x, self.y) pos = self.canvas.coords(self.id) if pos[1] <= 0: self.y = 3 if pos[3] >= self.canvas_height: self.y = -3 if self.hit_paddle(pos) == True: self.y = -3 if pos[0] <= 0: self.x = 3 if pos[2] >= self.canvas_width: self.x = -3 def hit_paddle(self, pos): paddle_pos = self.canvas.coords(self.paddle.id) if pos[2] >= paddle_pos[0] and pos[0] <= paddle_pos[2]: if pos[3] >= paddle_pos[1] and pos[3] <= paddle_pos[3]: return True return False class Paddle: def __init__(self, canvas, color): self.canvas = canvas self.id = canvas.create_rectangle(0, 0, 100, 10, fill=color) self.canvas.move(self.id, 200, 300) self.x = 0 self.canvas_width = self.canvas.winfo_width() self.canvas.bind_all('<KeyPress-Left>', self.turn_left) self.canvas.bind_all('<KeyPress-Right>', self.turn_right) def turn_left(self, evt): self.x = -2 def turn_right(self, evt): self.x = 2 def draw(self): self.canvas.move(self.id, self.x, 0) pos = self.canvas.coords(self.id) if pos[0] <= 0: self.x = 0 elif pos[2] >= self.canvas_width: self.x = 0 paddle = Paddle(canvas, 'blue') ball = Ball(canvas, paddle, 'red') while 1: ball.draw() paddle.draw() tk.update_idletasks() tk.update() time.sleep(0.01)
PyGame Basic Setup
import pygame import random WIDTH = 480 HEIGHT = 480 FPS = 30 # define colors WHITE = (255, 255, 255) BLACK = (0, 0, 0) RED = (255, 0, 0) GREEN = (0, 255, 0) BLUE = (0, 0, 255) # initialize pygame and create window pygame.init() pygame.mixer.init() screen = pygame.display.set_mode((WIDTH, HEIGHT)) pygame.display.set_caption("My Game") clock = pygame.time.Clock() # Game loop running = True while running: # keep loop running at the right speed clock.tick(FPS) # Process input (events) for event in pygame.event.get(): # check for closing window if event.type == pygame.QUIT: running = False # Update # Draw / render screen.fill(BLACK) # *after* drawing everything, flip the display pygame.display.flip() pygame.quit()
Python Turtle Demo Spirograph
<br> import random,time,turtle<br>bai = turtle.Turtle()<br>bai.pendown<br>bai.speed(10)<br>bai.tracer(300)<br>bai.hideturtle()<br>for i in range (1000):<br> for i in range(4):<br> for i in range (40):<br> bai.forward(50)<br> bai.left(100)<br> r = random.randint(0,255)<br> g = random.randint(0,255)<br> b = random.randint(0,255)<br> bai.pencolor((r,g,b))<br> for i in range(4):<br> bai.forward(10)<br> bai.left(90)<br> time.sleep(0.1)<br> bai.penup<br> bai.forward(100)<br> bai.pendown
<br><br> import turtle<br>import time<br>import random<br>bob = turtle.Turtle()<br>bob.tracer(300)<br>bob.pendown()<br>bob.hideturtle()<br>for i in range (20):<br> for i in range(200):<br> bob.forward(158)<br> bob.left(200)<br> bob.right(1)<br> r = random.randint(1,255)<br> g = random.randint(1,255)<br> b = random.randint(1,255)<br> bob.pencolor((r,g,b))<br> time.sleep(0.1) <br>
Hacking 3/12
PART 1
uname = “Mufasa”
password = “Circle Of Life”
realm = “testrealm@host.com“
nonce=” dcd98b7102dd2f0e8b11d0f600bfb0 c093″
uri=”/dir/index.html”
nc=”00000001″ # note this is a string
cnonce=”0a4f113b”
ha1 = hashlib.md5((uname+’:’+realm+’ :’+password).encode(‘utf-8’)). hexdigest()
ha2 = hashlib.md5((‘GET:’+uri). encode(‘utf-8’)).hexdigest()
response = hashlib.md5((ha1+’:’+nonce+’:’ +nc+’:’+cnonce+’:auth:’+ha2). encode(‘utf8’)).hexdigest()
print(response)
PART 2
from string import ascii_letters, digits
import itertools
import sys
for len in range(1,8):
for letters in itertools.product(ascii_ letters+digits, repeat=len):
guess=”.join(letters)
if happy_result(guess):
print(‘Password found:’, guess)
sys.exit()
print(‘Epic fail! Try harder next time.’)
RSA Encryption
woE7ewVfwoAzbwXCgC5iMyRvBTvCgGBiOy4=
public key: e=5, n=133
import random import base64 ''' Euclid's algorithm to determine the greatest common divisor ''' def gcd(a,b): while b != 0: c = a % b a = b b = c return a def egcd(a, b): if a == 0: return (b, 0, 1) g, y, x = egcd(b%a,a) return (g, x - (b//a) * y, y) def modinv(a, m): g, x, y = egcd(a, m) if g != 1: raise Exception('No modular inverse') return x%m def encrypt(plaintext,keypair): e,n = keypair # Encrypt the plaintext cipher = ''.join([chr(pow(ord(char),e,n)) for char in plaintext]) # Encode the ciphertext so it's more readable/sharable encoded = base64.b64encode(cipher.encode('utf-8')) return str(encoded,'utf-8') def decrypt(ciphertext,keypair): d,n = keypair # Decode the text to the original format decoded = base64.b64decode(ciphertext).decode('utf-8') # Decrypt it plain = (str(chr(pow(ord(char),d,n))) for char in decoded) return ''.join(plain) def generate_keypair(p,q,e=None): n = p * q #Phi is the totient of n phi = (p-1)*(q-1) #Choose an integer e such that e and phi(n) are coprime if e is None: e = random.randrange(1, phi) #Use Euclid's Algorithm to verify that e and phi(n) are comprime g = gcd(e, phi) while g != 1: e = random.randrange(1, phi) g = gcd(e, phi) #Now find the multiplicative inverse of e and phi to generate the private key d = modinv(e, phi) return ((e,n),(d,n)) #Only run this part if we're not running as an imported module if __name__ == '__main__': p = int(input("Enter prime number p: ")) q = int(input("Enter prime number q: ")) public, private = generate_keypair(p,q) print("Your public key is the number pair of (e=" + str(public[0]) + ", n=" + str(public[1]) +").\n") print("Your private key is the number pair of (d=" + str(private[0]) + ", n=" + str(private[1]) +").\n") s = input("Enter your message: ") encrypted = encrypt(s,public) print("Encrypted message: " + encrypted) decrypted = decrypt(encrypted,private) print("Decrypt: " + decrypted)