SpreadTooThin wrote:
> import array
> a = array.array('f', [1,2,3])
>
> print a.mean()
> print a.std_dev()
>
> Is there a way to calculate the mean and standard deviation on array
> data?
>
> Do I need to import it into a Numeric Array to do this?
>
>
I quickly fish this out of my functions toolbox. There's got to be
faster functions in scipy, though.
Frederic
(Disclaimer: If you build an air liner or a ocean liner with this and
the wings fall off at thirty thousand feet or it turns upside down in
the middle of an ocean, respectively of course, I expect a bunch of
contingency lawers lining up at my door wanting to sue you on my behalf.)
def standard_deviation (values):
"""
Takes a sequence and returns mean, variance and standard deviation.
Non-values (None) are skipped
"""
import math
mean = _sum_values_squared = _sum_values = 0.0
l = len (values)
i = 0
item_count = 0
while i < l:
value = values [i]
if value != None:
_sum_values += value
_sum_values_squared += value * value
item_count += 1
i += 1
if item_count < 2: # having skipped all Nones
return None, None, None
mean = _sum_values / item_count
variance = (_sum_values_squared - item_count * mean * mean) /
(item_count - 1)
if variance < 0.0: variance = 0.0
# Rounding errors can cause minute negative values which would crash
the sqrt
standard_deviation = math.sqrt (variance)
return mean, variance, standard_deviation
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