Random Class

This class implements functionalities associated with random number generation and pdf sampling. This class can sample and fill ranges with data corresponding to Gaussian, Exponential, Uniform and Breit-Wigner distributions.

This class is a wrapper of hydra C++ Random class. The Random class have two constructors to instantiate the Random class:

• Constructor with empty seed. The default seed value is 7895123.
• Constructor expecting the seed.

import HydraPython as hp

r = hp.Random()          # default seed
r2 = hp.Random(8385977)  # Seed value
# This will construct the 2 Random class's object one with default seed and
# one with the seed value 8385977

Apart from setting the seed value at the time of creation the seed can be set or get with setter and getter methods named SetSeed and GetSeed.

r = hp.Random()
print (r.GetSeed())         # Give the seed value of object r. 7895123
r.SetSeed(988763)           # New seed is 988763
print (r.GetSeed())         # 988763

The Random class provides a method named Uniform to generate the numbers between range (min, max) (both min and max exclusive) and fill them into the container according to the Continuous Uniform distribution. The container can be any of the host_vector_float or device_vector_float. In below examples, I have used device_vector_float extensively but they both can be used interchangeably in place of each other.

import HydraPython as hp

container = hp.device_vector_float(1000000)   # Continer to hold 1000000 objects
r = hp.Random()                              # Random number generator object
r.Uniform(1.1, 1.5, container)  # Minimum number 1.1, maximum 1.5 and container
# Above will generate 1000000 numbers between (1.1, 1.5)

print (container[:10])

The Gauss random number generation method can also be used with the Random class. The Gauss method generate the numbers with the given mean and sigma values.

import HydraPython as hp

container = hp.device_vector_float(1000000)   # Continer to hold 1000000 objects
r = hp.Random()                              # Random number generator object
r.Gauss(-2.0, 1.0, container)
# Above will generate 1000000 with mean -2.0 and sigma as 1.0

The Exponential random number generation method or Exp method in Random class generates the numbers with the given tau value of the Exponential distribution.

import HydraPython as hp

container = hp.host_vector_float(100)   # Continer to hold 100 objects.
r = hp.Random()                         # Random number generator object
r.Exp(1.0, container)                   # tau is 1.0
print (container)

The Random class also provides a BreitWigner method to generate random number according to a BreitWigner with mean and width.

import HydraPython as hp

container = hp.device_vector_float(10000)   # Continer to hold 10000 objects.
r = hp.Random()                             # Random number generator object
r.BreitWigner(2.0, 0.2, container)          # mean=2.0, width=0.2
print (container)

Apart from all these distributions, you can also define your own distribution and pass it as a function to the method. The Sample method allows you to pass a function that will be sampled for the given sampling range (lower, upper) and store the result in the container.

import HydraPython as hp

# The functon which will be sampled.
import math
def gauss1(*args):
    g = 1.0
    mean = -2.0
    sigma = 1.0
    for i in range(3):
        m2 = (args[i] - mean) * (args[i] - mean)
        s2 = sigma * sigma
        g *= math.e ** ((-m2/(2.0 * s2 ))/( math.sqrt(2.0*s2*math.pi)))
    return g

container = hp.host_vector_float3(10000)  # Container with 10000 objects each having 3 floats
r = hp.Random()                           # Random object
r.Sample(d, [6, 6, 6], [-6, -6, -6], gauss1)
# d is container, [6, 6, 6] is the start range (1 for each float in container),
# [-6, -6, -6] is end range, gauss1 is the functor.

In sample method, the start range and end range should have the same number of arguments as in the container. So for example, if you are using container of float7 than start range and end range each should contain 7 elements.

Warning

Any of device containers will not work with Sample method.

The complete method list supported by Random class can be found on [1].

The container list that can be passed to Sample method can be found on [2].

[1]

The method list for Random classes GetSeed Get the seed. Syntax: seed = r.GetSeed() SetSeed Set seed. Syntax: r.SetSeed(seed) Gauss Generate the Gauss distribution. Syntax: r.Gauss(mean, sigma, container) # container can be [device/host]_vector_float Uniform Generate the Continuous Uniform distribution. Syntax: r.Uniform(min, max, container) # container can be [device/host]_vector_float Exp Generate the Exponential distribution. Syntax: r.Exp(tau, container) # container can be [device/host]_vector_float BreitWigner Generate the BreitWigner distribution. Syntax: r.BreitWigner(mean, width, container) # container can be [device/host]_vector_float Sample sample the given function. Syntax: iterator_accepted_events = r.Sample(container, [min_values_list], [max_limit_list], function) # Container could be any of the container listed below

[2]

The list of available containers to use with Random. host_vector_float host container with 1 float. Syntax: h_container1 = hp.host_vector_float(size) host_vector_float2 host container with 2 float. Syntax: h_container2 = hp.host_vector_float2(size) host_vector_float3 host container with 3 float. Syntax: h_container3 = hp.host_vector_float3(size) host_vector_float4 host container with 4 float. Syntax: h_container4 = hp.host_vector_float4(size) host_vector_float5 host container with 5 float. Syntax: h_container5 = hp.host_vector_float5(size) host_vector_float6 host container with 6 float. Syntax: h_container6 = hp.host_vector_float6(size) host_vector_float7 host container with 7 float. Syntax: h_container7 = hp.host_vector_float7(size) host_vector_float8 host container with 8 float. Syntax: h_container8 = hp.host_vector_float8(size) host_vector_float9 host container with 9 float. Syntax: h_container9 = hp.host_vector_float9(size) host_vector_float10 host container with 10 float. Syntax: h_container10 = hp.host_vector_float10(size) device_vector_float device container with 1 float. Syntax: d_container1 = hp.device_vector_float(size) device_vector_float2 device container with 2 float. Syntax: d_container2 = hp.device_vector_float2(size) device_vector_float3 device container with 3 float. Syntax: d_container3 = hp.device_vector_float3(size) device_vector_float4 device container with 4 float. Syntax: d_container4 = hp.device_vector_float4(size) device_vector_float5 device container with 5 float. Syntax: d_container5 = hp.device_vector_float5(size) device_vector_float6 device container with 6 float. Syntax: d_container6 = hp.device_vector_float6(size) device_vector_float7 device container with 7 float. Syntax: d_container7 = hp.device_vector_float7(size) device_vector_float8 device container with 8 float. Syntax: d_container8 = hp.device_vector_float8(size) device_vector_float9 device container with 9 float. Syntax: d_container9 = hp.device_vector_float9(size) device_vector_float10 device container with 10 float. Syntax: d_container10 = hp.device_vector_float10(size)