feat(unitproc/bkt-remove_leading_zeroes):Add bash function

[BK-2020-03.git] / unitproc / python / sleepRand.py

#!/usr/bin/env python3
# Desc: Pauses a random amount of time. Random distribution is inverse gaussian.
# Version: 0.0.6
# Depends: python 3.7.3
# Usage: ./sleepRand.py [-v] [-p P] SECONDS
# Input: SECONDS: float seconds (mean of inverse gaussian distribution)
#        P:       precision (lambda of inverse gaussian distribution)
# Example: python3 sleepRand.py -vv -p 8.0 60.0

import argparse;
import math, time, random, sys;
import logging;

# Set up argument parser (see https://docs.python.org/3.7/library/argparse.html )
parser = argparse.ArgumentParser(
    description='Delay activity for a random number of seconds. Delays sampled from an inverse gaussian distribution.',
    epilog="Author: Steven Baltakatei Sandoval. License: GPLv3+");
parser.add_argument('-v','--verbose',
                    action='count',
                    dest='verbosity',
                    default=0,
                    help='Verbose output. (repeat for increased verbosity)');
parser.add_argument('mean',
                    action='store',
                    metavar='SECONDS',
                    nargs=1,
                    default=1,
                    type=float,
                    help='Mean seconds of delay. Is the mean of the inverse gaussian distribution.');
parser.add_argument('--precision','-p',
                    action='store',
                    metavar='P',
                    nargs=1,
                    default=[4.0],
                    type=float,
                    help='How concentrated delays are around the mean (default: 4.0). Must be a positive integer or floating point value. Is the lambda factor in the inverse gaussian distribution. High values (e.g. > 10.0) cause random delays to rarely stray far from MEAN. Small values (e.g. < 0.10) result in many small delays plus occasional long delays.');
parser.add_argument('--upper','-u',
                    action='store',
                    metavar='U',
                    nargs=1,
                    default=[None],
                    type=float,
                    help='Upper bound for possible delays (default: no bound). Without bound, extremely high delays are unlikely but possible.');
args = parser.parse_args();

# Define functions
def setup_logging(verbosity):
    '''Sets up logging'''
    # Depends: module: argparse
    # Ref/Attrib: Haas, Florian; Configure logging with argparse; https://xahteiwi.eu/resources/hints-and-kinks/python-cli-logging-options/
    base_loglevel = 30;
    verbosity = min(verbosity, 2);
    loglevel = base_loglevel - (verbosity * 10);
    logging.basicConfig(level=loglevel,
                        format='%(message)s');

def randInvGau(mu, lam):
    """Returns random variate of inverse gaussian distribution"""
    # input: mu:  mean of inverse gaussian distribution
    #        lam: shape parameter
    # output: float sampled from inv. gaus. with range 0 to infinity, mean mu
    # example: sample = float(randInvGau(1.0,4.0));
    # Ref/Attrib: Michael, John R. "Generating Random Variates Using Transformations with Multiple Roots" https://doi.org/10.2307/2683801
    nu = random.gauss(0,1);
    y = nu ** 2;
    xTerm1 = mu;
    xTerm2 = mu ** 2 * y / (2 * lam);
    xTerm3 = (- mu / (2 * lam)) * math.sqrt(4 * mu * lam * y + mu ** 2 * y ** 2);
    x = xTerm1 + xTerm2 + xTerm3;
    z = random.uniform(0.0,1.0);
    if z <= (mu / (mu + x)):
        return x;
    else:
        return (mu ** 2 / x);

# Process input
## Start up logger
setup_logging(args.verbosity);
logging.debug('DEBUG:Debug logging output enabled.');
logging.debug('DEBUG:args.verbosity:' + str(args.verbosity));
logging.debug('DEBUG:args:' + str(args));

## Receive input arguments
try:
    ### Get desired mean
    desMean = args.mean[0];    
    logging.debug('DEBUG:Desired mean:' + str(desMean));
    
    ### Get lambda precision factor
    lambdaFactor = args.precision[0];
    logging.debug('DEBUG:Lambda precision factor:' + str(lambdaFactor));
    
    ### Get upper bound
    if isinstance(args.upper[0], float):
        logging.debug('DEBUG:args.upper[0] is float:' + str(args.upper[0]));
        upperBound = args.upper[0];
    elif args.upper[0] is None:
        logging.debug('DEBUG:args.upper[0] is None:' + str(args.upper[0]));
        upperBound = None;
    else:
        raise TypeError('Upper bound not set correctly.');
    logging.debug('DEBUG:Upper bound:' + str(upperBound));
    
    ### Reject negative floats.
    if desMean < 0:
        logging.error('ERROR:Desired mean is negative:' + str(desMean));
        raise ValueError('Negative number error.');
    if lambdaFactor < 0:
        logging.error('ERROR:Lambda precision factor is negative:' + str(lambdaFactor));
        raise ValueError('Negative number error.');
except ValueError:
    sys.exit(1);

# Calculate delay
rawDelay = randInvGau(desMean, desMean * lambdaFactor);
logging.debug('DEBUG:rawDelay(seconds):' + str(rawDelay));
if isinstance(upperBound,float):
    delay = min(upperBound, rawDelay);
elif upperBound is None:
    delay = rawDelay;
logging.debug('DEBUG:delay(seconds)   :' + str(delay));

# Sleep
time.sleep(float(delay));

# Author: Steven Baltakatei Sandoal
# License: GPLv3+