def logNorm(self, step):
if len(self.data) >= self.maxNumb:
self.data = self.data[:self.maxNumb - 1]
self.data.insert(0, float(step))
else:
self.data.insert(0, float(step))
dist = NormalDist.from_samples(self.data)
return (step - dist.mean)/dist.stdev
def confidence_interval(data, confidence=0.95):
dist = NormalDist.from_samples(data)
z = NormalDist().inv_cdf((1 + confidence) / 2.)
h = dist.stdev * z / ((len(data) - 1)**.5)
return dist.mean, round((2 * h) / dist.mean, 4)
def calculate_confidence_interval(data, confidence=0.95):
dist = NormalDist.from_samples(data)
z = NormalDist().inv_cdf((1 + confidence) / 2.)
h = dist.stdev * z / ((len(data) - 1)**.5)
return dist.mean - h, dist.mean + h
