commit 27fe3f3b504ec59af6e4ea1bab9f991ef84be908 Author: Karsten Loesing karsten.loesing@gmx.net Date: Tue May 15 20:25:39 2018 +0200

Simplify censorship detector.

As part of the censorship detector, we're currently fitting a normal distribution to a set of approximately 50 values.

After talking to George Danezis, the original author of our censorship detector, it's safe to approximate this step by computing the mean and standard deviation of these values and using those to parameterize the normal distribution.

A test run with today's numbers produced the exact same result.

The main benefit of this simplification is that it will be easier for others to reproduce our numbers, even without a statistics library as powerful as SciPy. --- src/main/python/clients/detector.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/main/python/clients/detector.py b/src/main/python/clients/detector.py index 3d17bf0..9944710 100644 --- a/src/main/python/clients/detector.py +++ b/src/main/python/clients/detector.py @@ -43,6 +43,7 @@ import matplotlib

# Dep: numpy import numpy +from numpy import mean, std

# Dep: scipy import scipy.stats @@ -199,7 +200,8 @@ def make_tendencies_minmax(l, INTERVAL = 1): minx += [None] maxx += [None] continue - mu, signma = norm.fit(vals) + mu = mean(vals) + signma = std(vals) dists += [(mu, signma)] maxx += [norm.ppf(0.9999, mu, signma)] minx += [norm.ppf(1 - 0.9999, mu, signma)]