#!/usr/bin/python # -*- coding: utf-8 -*- """ cf. documentation cf http://docs.python.org/library/random.html random number generation - génération de nombres aléatoires functions of interest : choice, randint, seed """ from random import * facepiece = ['pile','face'] valeurpiece = [0.01,0.02,0.05,0.1,0.2,0.5,1.,2.] for i in range(1): # choice : random choice of an element from a list print(choice(facepiece), choice(valeurpiece)) # randint : return a random integer number between 2 values (including limits) print(randint(0,10)) # imprime un nombre aléatoire entre 0 et 10 print(choice(range(0,11,1))) # same function, using choice and range to create the list # seed(ANY_DATA) : seeding of the random number generator with any (constant) data # in order to generate reproducible random sequences. # seed() - without data - uses internal clock value to "randomly" initiate the generator ! for j in range(3): #seed('ma chaîne personnielle') # reproducible initialization seed() # to randomly initiate the generator for i in range(10): print(randint(1000,9999)) print(" ")
#!/usr/bin/env python # -*- coding: utf-8 -*- from random import * # cf. documentation cf http://docs.python.org/library/random.html import numpy as np import matplotlib.pyplot as plt # http://matplotlib.sourceforge.net/api/pyplot_api.html#module-matplotlib.pyplot import matplotlib.mlab as mlab # http://matplotlib.sourceforge.net/api/mlab_api.html#module-matplotlib.mlab #seed('ma chaîne personnelle') # reproducible initialization seed() rval = [] for j in range(100000): rval.append(randint(0,99)) # append to the list a random (integer) number between 0 and 99 # print rval # uncomment just to see the list of random numbers # analysis - histogram - see http://matplotlib.sourceforge.net/examples/api/histogram_demo.html # http://fr.wikipedia.org/wiki/Histogramme xh = np.array(rval) # see http://www.scipy.org/Cookbook/BuildingArrays transforme une liste en un tableau numérique de Numpy # print(xh) fig = plt.figure() ax = fig.add_subplot(111) n, bins, patches = ax.hist(xh, 50, facecolor='green', alpha=0.75) print(n) # les nombres d'occurences par classe print(bins) # les classes, de largeur identique # modifier le nombre de nombres générés, les nombres de classes-bins, plt.show()
#!/usr/bin/python # -*- coding: utf-8 -*- from tkinter import * import time window = Tk() sizex = 400 sizey = 200 canvas = Canvas(window, width = sizex, height = sizey) canvas.pack() x = 100 # initial left-most edge of first ball y = 30 # initial top-most edge of first ball r = 20 # ball diameter depx = 2 # displacement at each move in x direction depy = 1 # displacement at each move in y direction ball=canvas.create_oval(x,y,x+r,y+r,fill="blue") #moves no_moves = 140 for j in range(no_moves): canvas.move(ball, depx, depy) canvas.after(20) # time delay in milliseconds canvas.update() time.sleep(5) # on attend quelques secondes window.destroy()
#!/usr/bin/python # -*- coding: utf-8 -*- from tkinter import * import time from random import * window = Tk() sizex = 400 sizey = 600 canvas = Canvas(window, width = sizex, height = sizey) canvas.pack() x = 100 # initial left-most edge of first ball y = 30 # initial top-most edge of first ball r = 16 # ball diameter depx = 2 # displacement at each move in x direction depy = 0 # displacement at each move in y direction # create balls: no_particles = 20 dy = (sizey-2.*y)/(no_particles+1) # y initial separation between balls print(dy) ball_list = [] for i in range(no_particles): ball = canvas.create_oval(x,y,x+r,y+r,fill="blue") y = y+dy ball_list.append(ball) #moves no_moves = 100 for j in range(no_moves): for ball in ball_list: canvas.move(ball, depx, choice([-2, 2]) ) # canvas.move(ball, depx, depy) canvas.after(10) canvas.update() time.sleep(5) # on attend quelques secondes window.destroy()
#!/usr/bin/env python # -*- coding: utf-8 -*- from tkinter import * from random import choice # http://docs.python.org/library/random.html import numpy as np import matplotlib.pyplot as plt # http://matplotlib.sourceforge.net/api/pyplot_api.html#module-matplotlib.pyplot import matplotlib.mlab as mlab # http://matplotlib.sourceforge.net/api/mlab_api.html#module-matplotlib.mlab window = Tk() sizex = 200 sizey = 600 canvas = Canvas(window, width = sizex, height = sizey) canvas.pack() x = 100 # initial left-most edge of first ball y = 1 # initial top-most edge of first ball r = 4 # ball diameter depx = 10 # displacement at each move in x direction depy = 0 # create balls: no_particles = 6400 dy = (sizey-2.*y)/(no_particles+1) # y initial separation between balls print(dy) ball_list = [] for i in range(no_particles): ball = canvas.create_oval(x,y,x+r,y+r,fill="red") y = y+dy ball_list.append(ball) #moves no_moves = 6 # number of moves for j in range(no_moves): for ball in ball_list: canvas.move(ball, choice([-1,1])*depx, depy) canvas.after(1) canvas.update() #analysis - histogram # see http://matplotlib.sourceforge.net/examples/api/histogram_demo.html xpos=[] for ball in ball_list: posi = canvas.coords(ball) xpos.append(((no_moves+1.)/no_moves)*(posi[0]-x)/depx) # le facteur (no_moves+1.)/no_moves) permet de gérer la largeur des barres de l'histogramme xh = np.array(xpos) # see http://www.scipy.org/Cookbook/BuildingArrays #print(xh) fig = plt.figure() ax = fig.add_subplot(111) n, bins, patches = ax.hist(xh, (no_moves)+1, facecolor='green', alpha=0.75) print(n,bins, patches) plt.show() #window.mainloop()
#!/usr/bin/env python # -*- coding: utf-8 -*- from tkinter import * from random import choice # http://docs.python.org/library/random.html import numpy as np import matplotlib.pyplot as plt # http://matplotlib.sourceforge.net/api/pyplot_api.html#module-matplotlib.pyplot import matplotlib.mlab as mlab # http://matplotlib.sourceforge.net/api/mlab_api.html#module-matplotlib.mlab window = Tk() sizex = 400 sizey = 400 canvas = Canvas(window, width = sizex, height = sizey) canvas.pack() x = 200 # initial left-most edge of first ball y = 1 # initial top-most edge of first ball r = 4 # ball diameter depx = 1 # displacement at each move in x direction depy = 0 # create balls: no_particles = 1600 dy = (sizey-2.)/(no_particles+1) # y initial separation between balls print(dy) ball_list = [] for i in range(no_particles): ball = canvas.create_oval(x,y,x+r,y+r,fill="blue") y = y+dy ball_list.append(ball) #moves no_moves = 200 for j in range(no_moves): for ball in ball_list: canvas.move(ball, choice([-1,1])*depx, depy) canvas.after(1) canvas.update() #analysis - histogram # see http://matplotlib.sourceforge.net/examples/api/histogram_demo.html xpos = [] for ball in ball_list: posi = canvas.coords(ball) xpos.append((posi[0]-x)/depx) xh = np.array(xpos) # see http://www.scipy.org/Cookbook/BuildingArrays # compute the mean mu and sigma from xh (and/or theoretical value from random walk result) mu = np.mean(xh) sigma = np.std(xh) fig = plt.figure() ax = fig.add_subplot(111) # print xh n, bins, patches = ax.hist(xh, 10, facecolor='green', alpha=0.75) print(n,bins, patches) # hist uses np.histogram to create 'n' and 'bins'. cf. http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html ax.set_xlabel('X positions') ax.set_ylabel('Occurences') ax.grid(True) plt.show() #window.mainloop()
# -*- coding: utf-8 -*- from tkinter import * from random import choice # http://docs.python.org/library/random.html import numpy as np import matplotlib.pyplot as plt # http://matplotlib.sourceforge.net/api/pyplot_api.html#module-matplotlib.pyplot import matplotlib.mlab as mlab # http://matplotlib.sourceforge.net/api/mlab_api.html#module-matplotlib.mlab window = Tk() sizex = 400 sizey = 400 canvas = Canvas(window, width = sizex, height = sizey) canvas.pack() x = 200 # initial left-most edge of first ball y = 1 # initial top-most edge of first ball r = 4 # ball diameter depx = 1 # displacement at each move in x direction depy = 0 # create balls: no_particles = 1000 dy = (sizey-2.)/(no_particles+1) # y initial separation between balls #print dy ball_list=[] for i in range(no_particles): ball = canvas.create_oval(x,y,x+r,y+r,fill="blue") y = y+dy ball_list.append(ball) #moves no_moves = 400 for j in range(no_moves): for ball in ball_list: canvas.move(ball, choice([-1,-1,-1,-1,1,1,1,1,1,1])*depx, depy) #drift canvas.after(1) canvas.update() #analysis - histogram # see http://matplotlib.sourceforge.net/examples/api/histogram_demo.html xpos = [] for ball in ball_list: posi = canvas.coords(ball) xpos.append(posi[0]-x) xh = np.array(xpos) # see http://www.scipy.org/Cookbook/BuildingArrays # compute the mean mu and sigma from xh (and/or theoretical value from random walk result) mu = np.mean(xh) sigma = np.std(xh) fig = plt.figure() ax = fig.add_subplot(111) # print xh n, bins, patches = ax.hist(xh, 20, facecolor='green', alpha=0.75) print(mu, sigma) print(n,bins, patches) # hist uses np.histogram to create 'n' and 'bins'. # np.histogram returns the bin edges, so there will be ii probability # density values in n, ii+1 bin edges in bins and ii patches. To get # everything lined up, we'll compute the bin centers bincenters = 0.5*(bins[1:]+bins[:-1]) # add a 'best fit' line for the normal PDF yh = (bins[1]-bins[0])*no_particles*mlab.normpdf( bincenters, mu, sigma) # http://matplotlib.sourceforge.net/api/mlab_api.html#matplotlib.mlab.normpdf l = ax.plot(bincenters, yh, 'r--', linewidth=1) #print n ax.set_xlabel('X positions') ax.set_ylabel('Occurences') ax.grid(True) plt.show() #window.mainloop()