https://github.com/linsomniac/python-movingaverage

Function for computing a moving average of data.
https://github.com/linsomniac/python-movingaverage

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Function for computing a moving average of data.

• Host: GitHub
• URL: https://github.com/linsomniac/python-movingaverage
• Owner: linsomniac
• Created: 2011-02-05T22:41:52.000Z (almost 14 years ago)
• Default Branch: master
• Last Pushed: 2011-02-16T03:53:47.000Z (almost 14 years ago)
• Last Synced: 2023-07-27T11:54:59.885Z (over 1 year ago)
• Language: Python
• Homepage:
• Size: 91.8 KB
• Stars: 20
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README

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README

        
Written by Sean Reifschneider  

Released into the Public Domain

2011-02-05

Module to compute the moving average of a list.  For example:

   from movingaverage import movingaverage

   print list(movingaverage([1,2,3,4,5,6], 3))

   >>> [2,3,4,5]

NOTE: Another option is the "stats" pacakge, which is currently (Feb 2011)

in early development.  The author of that package says that it includes

several moving average functions.  I haven't used it, because I didn't find

out about it until after I wrote this.  For more information see:

http://pypi.python.org/pypi/stats/

movingaverage(data, subset_size, data_is_list = None, avoid_fp_drift = True)

   Return the moving averages of the data, with a window size of

   `subset_size`.  `subset_size` must be an integer greater than 0 and

   less than the length of the input data, or a ValueError will be raised.

   `data_is_list` can be used to tune the algorithm for list or iteratable

   as an input.  The default value, `None` will auto-detect this.

   The algorithm used if `data` is a list is almost twice as fast as if

   it is an iteratable.

   `avoid_fp_drift`, if True (the default) sums every sub-set rather than

   keeping a "rolling sum" (which may be subject to floating-point drift).

   While more correct, it is also dramatically slower for subset sizes

   much larger than 20.

   NOTE: You really should consider setting `avoid_fp_drift = False` unless

   you are dealing with very small numbers (say, far smaller than 0.00001)

   or require extreme accuracy at the cost of execution time.  For

   `subset_size` < 20, the performance difference is very small.