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@barun-saha barun-saha/_gen_stats.py
Last active Jan 11, 2019

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A simple module with a method to get the value of a statistic from the MessageStatsReport of the ONE simulator. Also provides a method to compute the 95% confidence interval from a set of sample values.
import csv
'''
A simple module with a method to get the value of a statistic from the MessageStatsReport of the ONE simulator. Also provides a method to compute the 95% confidence interval from a set of sample values.
'''
__author__ = "Barun Kumar Saha"
__copyright__ = "Copyright 2013, Barun Kumar Saha"
__license__ = "MIT"
__version__ = "1.0"
# Average of a list of numbers
def get_average(numbers = []):
avg = 0.0
n = len(numbers)
for i in xrange(0, n):
avg += numbers[i]
avg /= n
return avg
# Std. Dev. of a list of numbers
def get_std_dev(num = []):
n = len(num)
avg = get_average(num)
variance = 0.0
for i in xrange(0, n):
variance += (num[i] - avg) ** 2
variance /= n
std = variance ** 0.5
return std
# Get a named statistic from the MessageStats report file
def get_stat(file_name, stat_name = 'delivery_prob'):
result = 0.0
with open(file_name, 'r') as report:
reader = csv.reader(report, delimiter = ' ')
for line in reader:
if line[0].find(stat_name) == 0:
result = float(line[1])
break
return result
#
# t-distribution table
#
#Tail Probabilities
#One Tail 0.10 0.05 0.025 0.01 0.005 0.001 0.0005
#Two Tails 0.20 0.10 0.05 0.02 0.01 0.002 0.001
#-------+---------------------------------------------------------+-----
# D 1 | 3.078 6.314 12.71 31.82 63.66 318.3 637 | 1
# E 2 | 1.886 2.920 4.303 6.965 9.925 22.330 31.6 | 2
# G 3 | 1.638 2.353 3.182 4.541 5.841 10.210 12.92 | 3
# R 4 | 1.533 2.132 2.776 3.747 4.604 7.173 8.610 | 4
# E 5 | 1.476 2.015 2.571 3.365 4.032 5.893 6.869 | 5
# E 6 | 1.440 1.943 2.447 3.143 3.707 5.208 5.959 | 6
# S 7 | 1.415 1.895 2.365 2.998 3.499 4.785 5.408 | 7
# 8 | 1.397 1.860 2.306 2.896 3.355 4.501 5.041 | 8
# O 9 | 1.383 1.833 2.262 2.821 3.250 4.297 4.781 | 9
# F 10 | 1.372 1.812 2.228 2.764 3.169 4.144 4.587 | 10
# 11 | 1.363 1.796 2.201 2.718 3.106 4.025 4.437 | 11
# F 12 | 1.356 1.782 2.179 2.681 3.055 3.930 4.318 | 12
# R 13 | 1.350 1.771 2.160 2.650 3.012 3.852 4.221 | 13
# E 14 | 1.345 1.761 2.145 2.624 2.977 3.787 4.140 | 14
# E 15 | 1.341 1.753 2.131 2.602 2.947 3.733 4.073 | 15
# D 16 | 1.337 1.746 2.120 2.583 2.921 3.686 4.015 | 16
# O 17 | 1.333 1.740 2.110 2.567 2.898 3.646 3.965 | 17
# M 18 | 1.330 1.734 2.101 2.552 2.878 3.610 3.922 | 18
#
# Get CI of a mean
# Currently hard coded for sample size = 10, 95% CI
## 95% only
__t_values = {
1: 12.71,
2: 4.303,
3: 3.182,
4: 2.776,
5: 2.571,
6: 2.447,
7: 2.365,
8: 2.306,
9: 2.262,
10: 2.228,
11: 2.201,
12: 2.179,
13: 2.160,
14: 2.145,
15: 2.131,
16: 2.120,
17: 2.110,
18: 2.101,
}
def confidence_interval_mean(sample_size, sample_sd):
'''Only 95% CI'''
# If sample_size < 30 and population SD is unknown, use t distribution
# Else use std. normal distribution
delta = 0
root_n = sample_size ** 0.5
if sample_size < 30:
df = sample_size - 1
# t for 95% CI and df = 10 - 1 = 9
t = __t_values[df]
delta = t * sample_sd / root_n
else:
delta = 1.96 * sample_sd / root_n
return delta
@FuadYimer

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commented Jan 11, 2019

how can I use this code?
please help?

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