simple and small demo of python & numpy & matplotlib

python_demo.py

#python 
import random
import numpy
import matplotlib.pyplot as plt


# genere une liste de nombres aleatoires
# et affiche courbe de densite de probabilite
# http://www.science-emergence.com/Python/PythonFAQ/RandomNumberGaussPython/_source/
def demo0():
    #x = [random.randint(0., 100.) for i in range(10000)]
    x = [numpy.random.uniform(0., 100.) for i in range(10000)]
    num_bins = 200
    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
    plt.show()

#demo0()


# genere une liste de nombres aleatoires avec une fonction gaussiene
# et affiche courbe de densite de probabilite
# http://www.science-emergence.com/Python/PythonFAQ/RandomNumberGaussPython/_source/
def demo1():
    x = [random.gauss(100,15) for i in range(1000)]
    num_bins = 50
    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
    plt.show()

#demo1()


# la meme avec une distribution gamma
# et affiche courbe de densite de probabilite
# https://fr.wikipedia.org/wiki/Loi_Gamma
# https://en.wikipedia.org/wiki/Gamma_distribution
def demo2():
    x = numpy.random.gamma(2., 2., 10000)
    #x = numpy.random.gamma(1., 2., 10000)
    #x = numpy.random.gamma(3., 2., 10000)
    #x = numpy.random.gamma(5., 1., 10000)
    #x = numpy.random.gamma(9., .5, 10000)
    num_bins = 50
    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
    plt.show()

#demo2()


def demo3():
    # https://www.daniweb.com/programming/software-development/threads/199275/how-can-i-create-a-list-of-randomly-generated-tuples
    #x = [(random.randint(low, high), ) for k in range(10)]
    #x = [random.gauss(80,20) for i in range(1000)]
    #x = numpy.random.gamma(2., 2., 10000)
    x = [55 + (8. * random.gammavariate(3,2)) for i in range(10000)]
    num_bins = 50
    n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
    plt.show()

#demo3()