sample-rng-plot.py

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#
# SPDX-License-Identifier: GPL-2.0-only
#
 
## @file
#  @ingroup core-examples
#  @ingroup randomvariable
#  Demonstrate use of ns-3 as a random number generator integrated with
#  plotting tools.
#
#  This is adapted from Gustavo Carneiro's ns-3 tutorial
 
 
import argparse
import sys
 
import matplotlib.pyplot as plt
import numpy as np
 
## Import ns-3
try:
    from ns import ns
except ModuleNotFoundError:
    raise SystemExit(
        "Error: ns3 Python module not found;"
        " Python bindings may not be enabled"
        " or your PYTHONPATH might not be properly configured"
    )
 
 
def main():
    parser = argparse.ArgumentParser("sample-rng-plot")
    parser.add_argument("--not-blocking", action="store_true", default=False)
    args = parser.parse_args(sys.argv[1:])
 
    # mu, var = 100, 225
 
    ## Random number generator.
    rng = ns.CreateObject[ns.NormalRandomVariable]()
    rng.SetAttribute("Mean", ns.DoubleValue(100.0))
    rng.SetAttribute("Variance", ns.DoubleValue(225.0))
 
    ## Random number samples.
    x = [rng.GetValue() for t in range(10000)]
 
    # the histogram of the data
 
    ## Make a probability density histogram
    density = 1
    ## Plot color
    facecolor = "g"
    ## Plot alpha value (transparency)
    alpha = 0.75
 
    # We don't really need the plot results, we're just going to show it later.
    # n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)
    n, bins, patches = plt.hist(x, 50, density=True, facecolor="g", alpha=0.75)
 
    plt.title("ns-3 histogram")
    plt.text(60, 0.025, r"$\mu=100,\ \sigma=15$")
    plt.axis([40, 160, 0, 0.03])
    plt.grid(True)
    plt.show(block=not args.not_blocking)
 
 
if __name__ == "__main__":
    main()