feat(unitproc/bkt-remove_leading_zeroes):Add bash function
[BK-2020-03.git] / unitproc / python / sleepRand.py
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1#!/usr/bin/env python3
2# Desc: Pauses a random amount of time. Random distribution is inverse gaussian.
31322bd9 3# Version: 0.0.6
ae63d7db 4# Depends: python 3.7.3
a967b286 5# Usage: ./sleepRand.py [-v] [-p P] SECONDS
787f6bbd 6# Input: SECONDS: float seconds (mean of inverse gaussian distribution)
a967b286 7# P: precision (lambda of inverse gaussian distribution)
787f6bbd 8# Example: python3 sleepRand.py -vv -p 8.0 60.0
ae63d7db 9
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10import argparse;
11import math, time, random, sys;
12import logging;
ae63d7db 13
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14# Set up argument parser (see https://docs.python.org/3.7/library/argparse.html )
15parser = 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+");
18parser.add_argument('-v','--verbose',
19 action='count',
20 dest='verbosity',
21 default=0,
22 help='Verbose output. (repeat for increased verbosity)');
23parser.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.');
30parser.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.');
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37parser.add_argument('--upper','-u',
38 action='store',
39 metavar='U',
40 nargs=1,
41 default=[None],
42 type=float,
43 help='Upper bound for possible delays (default: no bound). Without bound, extremely high delays are unlikely but possible.');
787f6bbd 44args = parser.parse_args();
0e82b3c2 45
ae63d7db 46# Define functions
787f6bbd 47def setup_logging(verbosity):
a967b286 48 '''Sets up logging'''
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49 # Depends: module: argparse
50 # Ref/Attrib: Haas, Florian; Configure logging with argparse; https://xahteiwi.eu/resources/hints-and-kinks/python-cli-logging-options/
51 base_loglevel = 30;
52 verbosity = min(verbosity, 2);
53 loglevel = base_loglevel - (verbosity * 10);
54 logging.basicConfig(level=loglevel,
55 format='%(message)s');
56
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57def randInvGau(mu, lam):
58 """Returns random variate of inverse gaussian distribution"""
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59 # input: mu: mean of inverse gaussian distribution
60 # lam: shape parameter
61 # output: float sampled from inv. gaus. with range 0 to infinity, mean mu
62 # example: sample = float(randInvGau(1.0,4.0));
787f6bbd 63 # Ref/Attrib: Michael, John R. "Generating Random Variates Using Transformations with Multiple Roots" https://doi.org/10.2307/2683801
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64 nu = random.gauss(0,1);
65 y = nu ** 2;
66 xTerm1 = mu;
67 xTerm2 = mu ** 2 * y / (2 * lam);
68 xTerm3 = (- mu / (2 * lam)) * math.sqrt(4 * mu * lam * y + mu ** 2 * y ** 2);
69 x = xTerm1 + xTerm2 + xTerm3;
70 z = random.uniform(0.0,1.0);
71 if z <= (mu / (mu + x)):
72 return x;
73 else:
74 return (mu ** 2 / x);
75
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76# Process input
77## Start up logger
78setup_logging(args.verbosity);
79logging.debug('DEBUG:Debug logging output enabled.');
80logging.debug('DEBUG:args.verbosity:' + str(args.verbosity));
81logging.debug('DEBUG:args:' + str(args));
a967b286 82
31322bd9 83## Receive input arguments
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84try:
85 ### Get desired mean
86 desMean = args.mean[0];
87 logging.debug('DEBUG:Desired mean:' + str(desMean));
31322bd9 88
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89 ### Get lambda precision factor
90 lambdaFactor = args.precision[0];
91 logging.debug('DEBUG:Lambda precision factor:' + str(lambdaFactor));
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92
93 ### Get upper bound
94 if isinstance(args.upper[0], float):
95 logging.debug('DEBUG:args.upper[0] is float:' + str(args.upper[0]));
96 upperBound = args.upper[0];
97 elif args.upper[0] is None:
98 logging.debug('DEBUG:args.upper[0] is None:' + str(args.upper[0]));
99 upperBound = None;
100 else:
101 raise TypeError('Upper bound not set correctly.');
102 logging.debug('DEBUG:Upper bound:' + str(upperBound));
103
104 ### Reject negative floats.
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105 if desMean < 0:
106 logging.error('ERROR:Desired mean is negative:' + str(desMean));
107 raise ValueError('Negative number error.');
108 if lambdaFactor < 0:
109 logging.error('ERROR:Lambda precision factor is negative:' + str(lambdaFactor));
110 raise ValueError('Negative number error.');
111except ValueError:
112 sys.exit(1);
ae63d7db 113
ae63d7db 114# Calculate delay
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115rawDelay = randInvGau(desMean, desMean * lambdaFactor);
116logging.debug('DEBUG:rawDelay(seconds):' + str(rawDelay));
117if isinstance(upperBound,float):
118 delay = min(upperBound, rawDelay);
119elif upperBound is None:
120 delay = rawDelay;
121logging.debug('DEBUG:delay(seconds) :' + str(delay));
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122
123# Sleep
124time.sleep(float(delay));
125
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126# Author: Steven Baltakatei Sandoal
127# License: GPLv3+