Analyzing Data with Statistics Data Generator

We can create a simple python script to generate a stream of Gaussian noise at the frequency of one message per second as a python script which should be saved at `~/rand_gen.py`.

```#!/usr/bin/python
import random
import sys
import time
def main():
mu = float(sys.argv)
sigma = float(sys.argv)
freq_s = int(sys.argv)
while True:
print str(random.gauss(mu, sigma))
sys.stdout.flush()
time.sleep(freq_s)

if __name__ == '__main__':
main()```

This script will take the following as arguments:

• The mean of the data generated

• The standard deviation of the data generated

• The frequency (in seconds) of the data generated

If, however, you'd like to test a longer tailed distribution, like the student t-distribution and have numpy installed, you can use the following as `~/rand_gen.py`:

```#!/usr/bin/python
import random
import sys
import time
import numpy as np

def main():
df = float(sys.argv)
freq_s = int(sys.argv)
while True:
print str(np.random.standard_t(df))
sys.stdout.flush()
time.sleep(freq_s)

if __name__ == '__main__':
main()```

This script will take the following as arguments:

• The degrees of freedom for the distribution

• The frequency (in seconds of the data generated