Generating Random Numbers in Python
This guide discusses using Python to generate random numbers in a certain range.
Python comes with a random number generator which can be used to generate various distributions of numbers. These random number generators are suitable for generating numbers for spacial and graphical distributions. To access the random number generators, the
random module must be imported.
IMPORTANT NOTE: The pseudo-random generators of this module should not be used for security purposes. Use
SystemRandom if you require a cryptographically secure pseudo-random number generator.
Simple Random numbers
The basic method to create a random number from the imported
random module is with
import random ran_number = random.random() print ran_number
The output from the above code is a random number between 0 and 1, say 0.280268102083 The number can be used directly or to arrive at some other number:
import random import rhinoscriptsyntax as rs ran_number = random.random() degrees = ran_number*360 id = rs.GetObject("Select an object to rotate randomly around the CPlane origin") if id: rs.RotateObject(id,[0,0,0], degrees)
random.uniform() to generate a random number between two numbers other than zero and 1:
import random low = 4; high = 8 ran_number = random.uniform(low, high) print ran_number
The above would generate a random number between 4 and 8. Note for random.uniform low and high numbers must be specified.
Advanced Random numbers
For graphic and special random distributions, many times better distributions can be generated using one of the advanced distributions. These random numbers can generate cumulative distribution functions. There are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is also available.
For more detailed information on these advanced number generators go to the Generate pseudo-random numbers documentation
The random module contains functions for finding random integers, selection of a random element from a list, a function to generate a random permutation of a list in-place, for random sampling with and without replacement, etc.
The Random number generator may also be started with a seed number.
random.seed(seed_number) When a seed is used, the generator can create a repeatable set of pseudo-random numbers. If repeatability is important, this may be worth using. Normally the current system time is used which leads to a different solution each time.
The random functions also have the ability to choose a random element from a list. The following code will print a random selection from the string containing the alphabet. Note that processing a string of characters is equivalent to processing a list of the characters in the string.
import random print random.choice('abcdefghijklmnopqrstuvmxyz') ```` `random` can also generate a set of samples from a larger list of values. The following code will return 3 randomly chosen samples from the list of numbers. ```python import random my_list = [1, 2, 3, 4, 5] my_samp = 3 print random.sample(my_list, my_samp)
For more detailed information on these advanced methods go to the Generate pseudo-random numbers documentation
Os and System Random
For a random number generator that does not rely on the software state and for which the sequences are not reproducible, use the SystemRandom method. This function returns random bytes from an OS-specific randomness source.
import sys import random key_num = random.SystemRandom() print key_num.random() #produces a number between 0 and 1 print key_num.randint(0, sys.maxint) # produces a integer between 0 and the highest allowed by the OS.