/usr/lib/python3/dist-packages/pygal/stats.py is in python3-pygal 2.4.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | from math import log, pi, sqrt
def erfinv(x, a=.147):
"""Approximation of the inverse error function
https://en.wikipedia.org/wiki/Error_function
#Approximation_with_elementary_functions
"""
lnx = log(1 - x * x)
part1 = (2 / (a * pi) + lnx / 2)
part2 = lnx / a
sgn = 1 if x > 0 else -1
return sgn * sqrt(sqrt(part1 * part1 - part2) - part1)
def norm_ppf(x):
if not 0 < x < 1:
raise ValueError("Can't compute the percentage point for value %d" % x)
return sqrt(2) * erfinv(2 * x - 1)
def ppf(x, n):
try:
from scipy import stats
except ImportError:
stats = None
if stats:
if n < 30:
return stats.t.ppf(x, n)
return stats.norm.ppf(x)
else:
if n < 30:
# TODO: implement power series:
# http://eprints.maths.ox.ac.uk/184/1/tdist.pdf
raise ImportError(
'You must have scipy installed to use t-student '
'when sample_size is below 30')
return norm_ppf(x)
# According to http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/
# BS704_Confidence_Intervals/BS704_Confidence_Intervals_print.html
def confidence_interval_continuous(
point_estimate, stddev, sample_size, confidence=.95, **kwargs):
"""Continuous confidence interval from sample size and standard error"""
alpha = ppf((confidence + 1) / 2, sample_size - 1)
margin = stddev / sqrt(sample_size)
return (point_estimate - alpha * margin, point_estimate + alpha * margin)
def confidence_interval_dichotomous(
point_estimate, sample_size, confidence=.95, bias=False,
percentage=True, **kwargs):
"""Dichotomous confidence interval from sample size and maybe a bias"""
alpha = ppf((confidence + 1) / 2, sample_size - 1)
p = point_estimate
if percentage:
p /= 100
margin = sqrt(p * (1 - p) / sample_size)
if bias:
margin += .5 / sample_size
if percentage:
margin *= 100
return (point_estimate - alpha * margin, point_estimate + alpha * margin)
def confidence_interval_manual(point_estimate, low, high):
return (low, high)
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