"""

Statistics module for MicroPython (with low memory usage):

https://github.com/rcolistete/MicroPython_Statistics

Version: 0.5.0 @ 2018/10/24

Author: Roberto Colistete Jr. (roberto.colistete at gmail.com)

License: MIT License (https://opensource.org/licenses/MIT)

"""

import math

def mean(data):

if iter(data) is data:

data = list(data)

return sum(data)/len(data)

def harmonic_mean(data):

if iter(data) is data:

data = list(data)

return len(data)/sum([1/x for x in data])

def median(data):

data = sorted(data)

n = len(data)

if n % 2 == 1:

return data[n//2]

else:

i = n//2

return (data[i - 1] + data[i])/2

def median_low(data):

data = sorted(data)

n = len(data)

if n % 2 == 1:

return data[n//2]

else:

return data[n//2 - 1]

def median_high(data):

data = sorted(data)

n = len(data)

return data[n//2]

def median_grouped(data, interval=1):

data = sorted(data)

n = len(data)

x = data[n//2]

L = x - interval/2

l1 = l2 = n//2

while (l1 > 0) and (data[l1 - 1] == x):

l1 -= 1

while (l2 < n) and (data[l2 + 1] == x):

l2 += 1

return L + (interval*(n/2 - l1)/(l2 - l1 + 1))

def mode(data):

if iter(data) is data:

data = list(data)

data = sorted(data)

last = modev = None

countmax = i = 0

while i < len(data):

if data[i] == last:

count += 1

else:

count = 1

last = data[i]

if count > countmax:

countmax = count

modev = last

i += 1

return modev

def _ss(data, c=None):

if c is None:

c = mean(data)

total = total2 = 0

for x in data:

total += (x - c)**2

total2 += (x - c)

total -= total2**2/len(data)

return total

def variance(data, xbar=None):

if iter(data) is data:

data = list(data)

return _ss(data, xbar)/(len(data) - 1)

def pvariance(data, mu=None):

if iter(data) is data:

data = list(data)

return _ss(data, mu)/len(data)

def stdev(data, xbar=None):

return math.sqrt(variance(data, xbar))

def pstdev(data, mu=None):

return math.sqrt(pvariance(data, mu))