Seite - 55 - in Programming for Computations – Python - A Gentle Introduction to Numerical Simulations with Python 3.6, Band Second Edition

2.4 RandomNumbers 55 In [3]: random.randint(1, 6) Out[3]: 5 The random module contains also other useful functions, two of which are random (yes, same name as the module) and uniform. Both of these functions return a floating point number from an interval where each number has equal probability of being drawn. For random, the interval is always [0,1) (i.e. 0 is included, but 1 is not), while uniform requires the programmer to specify the interval [a,b] (wherebotha andbare included14).Thefunctionsareusedsimilarly torandint, so, interactively,wemayforexampledo: In [1]: import random In [2]: x = random.random() # draw float from [0, 1), assign to x In [3]: y = random.uniform(10, 20) # ...float from [10, 20], assign to y In [4]: print(’x = {:g}, y = {:g}’.format(x, y)) Out[5]: x = 0.714621 , y = 13.1233 Drawing Many Random Numbers at a Time You have now met three useful functions from the randommodule in Python’s standard library and seen them in simple use. However, each of those functions provides only a single number with each functioncall. If you need manypseudo-randomnumbers,one option is to use such function calls inside a loop (Chap.3). Another (faster) alternative, is to rather usefunctionsthatallowvectorizeddrawingof thenumbers,so thatasinglefunction call provides all the numbers you need in one go. Such functionality is offered by another module, which also happens to be calledrandom, but which resides in the numpy library. All three functions demonstrated above have their counterparts in numpyand we might show interactivelyhoweach of these can be used to generate, e.g., fournumberswithone functioncall. In [1]: import numpy as np In [2]: np.random.randint(1, 6, 4) # ...4 integers from [1, 6) Out[2]: array([1, 3, 5, 3]) In [3]: np.random.random(4) # ...4 floats from [0, 1) Out[3]: array([ 0.79183276, 0.01398365, 0.04982849, 0.11630963]) In [4]: np.random.uniform(10, 20, 4) # ...4 floats from [10, 20) Out[4]: array([ 10.95846078, 17.3971301 , 19.73964488, 18.14332234]) In each case, the size argument is here set to 4 and an array is returned.Of course, with the size argument, you may ask for thousands of numbers if you like. As is evident fromthe interval specifications in the code,noneof these functions include the upper interval limit. However, if we wanted, e.g., randint to have 6 as the inclusiveupper limit, wecouldsimplygive7as thesecondargument in stead. 14 Strictlyspeaking,bmayormaynotbeincluded(http://docs.python.org/),dependingonfloating- point rounding in the equationa + (b-a)*random().