#!/usr/bin/env python3
# Desc: Pauses a random amount of time. Random distribution is inverse gaussian.
-# Version: 0.0.1
+# Version: 0.0.6
# Depends: python 3.7.3
-# Usage: ./sleepRand.py arg1
-# Input: arg1: float seconds (mean of inverse gaussian distribution)
-# Example: python3 sleepRand.py 4.0
+# 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 math, time, random, sys
+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"""
- # Ref/Attrib: doi:10.1080/00031305.1976.10479147
+ # 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;
else:
return (mu ** 2 / x);
-# Check input (TODO)
-arg1 = float(sys.argv[1]); # first argument
-desMean = arg1;
+# 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));
-# Configure
-lambdaFactor = 4; # spread factor; inversely proportional to variance
+## 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
-delay = randInvGau(desMean, desMean * lambdaFactor);
-#print('DEBUG:delay:' + str(float(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+
projects / BK-2020-03.git / blobdiff