Getting Random Numbers From A Truncated Maxwell-boltzmann Distribution

I would like to generate random numbers using a truncated Maxwell-Boltzmann distribution. I know that scipy has built-in Maxwell random variables, but there is no truncated version

Solution 1:

There are several things you can do here.

• For fixed parameters v_esc and v_0, n_0 is a constant, so it doesn't need to be calculated in the pdf method.
• If you define only a PDF for a SciPy rv_continuous subclass, then the class's rvs, mean, and so on will be very slow, presumably because the method needs to integrate the PDF every time it needs to generate a random variate or calculate a statistic. If speed is at a premium, you will thus need to add to maxwell_boltzmann_pdf an _rvs method that uses its own sampler. (See also this question.) One possible method is the rejection sampling method: Draw a number in a box until the box falls within the PDF. It works for any bounded PDF with a finite domain, as long as you know what the domain and bound are (the bound is the maximum value of f in the domain). See this question for example Python code.
• If you know the distribution's CDF, then there are some additional tricks. One of them is the relatively new k-vector sampling method for sampling a continuous distribution. There are two phases: a setup phase and a sampling phase. The setup phase involves approximating the CDF's inverse via root finding, and the sampling phase uses this approximation to generate random numbers that follow the distribution in a very fast way without having to further evaluate the CDF. For a fixed distribution like this one, if you show me the CDF, I can precalculate the necessary data and the code needed to sample the distribution using that data. Essentially, the only non-trivial part of k-vector sampling is the root-finding step.
• More information on sampling from an arbitrary distribution is found on my sampling methods page.

Solution 2:

It turns out that there is a way to generate a truncated Maxwell-Boltzmann distribution with the inverse transform sampling method using the ppf feature of scipy. I am posting the code here for future reference.

import matplotlib.pyplot as plt
import numpy as np
from scipy.special import erf
from scipy.stats import maxwell

# parameters
v_0 = 220#km/s
v_esc = 550#km/s
N = 10000# CDF(v_esc)
cdf_v_esc = maxwell.cdf(v_esc,scale=v_0/np.sqrt(2))

# pdf for the distributiondeff_MB(v_mag):
    n_0 = np.power(np.pi,3/2)*np.square(v_0)*(v_0*erf(v_esc/v_0)-(2/np.sqrt(np.pi))*v_esc*np.exp(-np.square(v_esc/v_0)))
    return (1/n_0)*(4*np.pi*np.square(v_mag))*np.exp(-np.square(v_mag/v_0))*np.heaviside(v_esc-v_mag,0)

# plot the pdf
x = np.arange(600)
y = [f_MB(i) for i in x]
plt.plot(x,y,label='pdf')

# use inverse transform sampling to get the truncated Maxwell-Boltzmann distribution
sample = maxwell.ppf(np.random.rand(N)*cdf_v_esc,scale=v_0/np.sqrt(2))

# plot the histogram of the samples
plt.hist(sample,bins=100,histtype='step',density=True,label='histogram')
plt.xlabel('v_mag')
plt.legend()
plt.show()

This code generates the required random variables and compare its histogram with the analytic form of the pdf, which matches with each other pretty well.