39175065a96362c4a613004dcf2004143287e217
[BK-2020-03.git] / unitproc / python / sleepRand.py
1 #!/usr/bin/env python3
2 # Desc: Pauses a random amount of time. Random distribution is inverse gaussian.
3 # Version: 0.0.4
4 # Depends: python 3.7.3
5 # Usage: ./sleepRand.py [-v] [-p L] SECONDS
6 # Input: SECONDS: float seconds (mean of inverse gaussian distribution)
7 # L: precision (lambda of inverse gaussian distribution)
8 # Example: python3 sleepRand.py -vv -p 8.0 60.0
9
10 import argparse;
11 import math, time, random, sys;
12 import logging;
13
14 # Set up argument parser (see https://docs.python.org/3.7/library/argparse.html )
15 parser = argparse.ArgumentParser(
16 description='Delay activity for a random number of seconds. Delays sampled from an inverse gaussian distribution.',
17 epilog="Author: Steven Baltakatei Sandoval. License: GPLv3+");
18 parser.add_argument('-v','--verbose',
19 action='count',
20 dest='verbosity',
21 default=0,
22 help='Verbose output. (repeat for increased verbosity)');
23 parser.add_argument('mean',
24 action='store',
25 metavar='SECONDS',
26 nargs=1,
27 default=1,
28 type=float,
29 help='Mean seconds of delay. Is the mean of the inverse gaussian distribution.');
30 parser.add_argument('--precision','-p',
31 action='store',
32 metavar='P',
33 nargs=1,
34 default=[4.0],
35 type=float,
36 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.');
37 args = parser.parse_args();
38
39 # Define functions
40 def setup_logging(verbosity):
41 # Depends: module: argparse
42 # Ref/Attrib: Haas, Florian; Configure logging with argparse; https://xahteiwi.eu/resources/hints-and-kinks/python-cli-logging-options/
43 base_loglevel = 30;
44 verbosity = min(verbosity, 2);
45 loglevel = base_loglevel - (verbosity * 10);
46 logging.basicConfig(level=loglevel,
47 format='%(message)s');
48
49 def randInvGau(mu, lam):
50 """Returns random variate of inverse gaussian distribution"""
51 # input: mu: mean of inverse gaussian distribution
52 # lam: shape parameter
53 # output: float sampled from inv. gaus. with range 0 to infinity, mean mu
54 # example: sample = float(randInvGau(1.0,4.0));
55 # Ref/Attrib: Michael, John R. "Generating Random Variates Using Transformations with Multiple Roots" https://doi.org/10.2307/2683801
56 nu = random.gauss(0,1);
57 y = nu ** 2;
58 xTerm1 = mu;
59 xTerm2 = mu ** 2 * y / (2 * lam);
60 xTerm3 = (- mu / (2 * lam)) * math.sqrt(4 * mu * lam * y + mu ** 2 * y ** 2);
61 x = xTerm1 + xTerm2 + xTerm3;
62 z = random.uniform(0.0,1.0);
63 if z <= (mu / (mu + x)):
64 return x;
65 else:
66 return (mu ** 2 / x);
67
68 # Process input
69 ## Start up logger
70 setup_logging(args.verbosity);
71 logging.debug('DEBUG:Debug logging output enabled.');
72 logging.debug('DEBUG:args.verbosity:' + str(args.verbosity));
73 logging.debug('DEBUG:args:' + str(args));
74 ## Reject negative floats.
75 try:
76 ### Get desired mean
77 desMean = args.mean[0];
78 logging.debug('DEBUG:Desired mean:' + str(desMean));
79 ### Get lambda precision factor
80 lambdaFactor = args.precision[0];
81 logging.debug('DEBUG:Lambda precision factor:' + str(lambdaFactor));
82 if desMean < 0:
83 logging.error('ERROR:Desired mean is negative:' + str(desMean));
84 raise ValueError('Negative number error.');
85 if lambdaFactor < 0:
86 logging.error('ERROR:Lambda precision factor is negative:' + str(lambdaFactor));
87 raise ValueError('Negative number error.');
88 except ValueError:
89 sys.exit(1);
90
91 # Calculate delay
92 delay = randInvGau(desMean, desMean * lambdaFactor);
93 logging.debug('delay:' + str(delay));
94
95 # Sleep
96 time.sleep(float(delay));
97
98 # Author: Steven Baltakatei Sandoal
99 # License: GPLv3+