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"""
The statistics module.
This module contains miscellaneous stastistical functions for expyriment.
"""
__author__ = 'Florian Krause <florian@expyriment.org>, \
Oliver Lindemann <oliver@expyriment.org>'
__version__ = '0.7.0'
__revision__ = '55a4e7e'
__date__ = 'Wed Mar 26 14:33:37 2014 +0100'
def sum(data):
"""Returns the sum of data.
The function ignores all non-numerical elements in the data and returns
None if no numerical element has been found. In contrast to standard math
and numpy functions, this function is robust against type violations.
Parameters
----------
data : list
list of numerical data
Returns
-------
out : float or None
"""
s = 0
elem_found = False
for v in data:
try:
s += v
elem_found = True
except:
pass
if elem_found:
return s
else:
return None
def mode(data):
"""Returns the mode, that is, the most frequent value in data.
Parameters
----------
data : list
list of numerical data
Returns
-------
out : float or None
"""
freq = frequence_table(data)
Fmax = max(freq.values())
for x, f in freq.items():
if f == Fmax:
break
return x
def mean(data):
"""Returns the mean of data.
Notes
-----
The function ignores all non-numerical elements in the data and returns
None if no numerical element has been found. In contrast to standard math
and numpy functions, this function is robust against type violations.
Parameters
----------
data : list
list of numerical data
Returns
-------
out : float or None
"""
s = 0
cnt = 0
for v in data:
try:
s += v
cnt += 1
except:
pass
if cnt == 0:
return None
else:
return float(s) / float(cnt)
def median(data):
"""Returns the median of data.
Notes
-----
The function ignores all non-numerical elements in the data and returns
None if no numerical element has been found. In contrast to standard math
and numpy functions, this function is robust against type violations.
Parameters
----------
data : list
list of numerical data
Returns
-------
out : float or None
"""
tmp = []
for elem in data: # remove non numerics
if isinstance(elem, (int, long, float)):
tmp.append(elem)
data = sorted(tmp)
if len(data) % 2 == 1:
return data[(len(data) - 1) / 2 ]
else:
lower = data[len(data) / 2 - 1]
upper = data[len(data) / 2]
return (float(lower + upper)) / 2.0
def frequence_table(data):
"""Returns the frequency table of the data as dictionary.
Parameters
----------
data : list
list of numerical data
Returns
-------
out : dict
`dict.keys` : values, `dict.values` : frequencies
"""
freq = {}
for x in data:
freq[x] = freq.get(x, 0) + 1
return freq
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