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#!/usr/bin/env python
#
# Sean Reifschneider, tummy.com, ltd. <jafo@tummy.com>
# Released into the Public Domain, 2011-02-06
import itertools
from itertools import islice
from collections import deque
#########################################################
def 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.
'''
if subset_size < 1:
raise ValueError('subset_size must be 1 or larger')
if data_is_list is None:
data_is_list = hasattr(data, '__getslice__')
divisor = float(subset_size)
if data_is_list:
# This only works if we can re-access old elements, but is much faster.
# In other words, it can't be just an iterable, it needs to be a list.
if subset_size > len(data):
raise ValueError('subset_size must be smaller than data set size')
if avoid_fp_drift:
for x in range(subset_size, len(data) + 1):
yield sum(data[x - subset_size:x]) / divisor
else:
cur = sum(data[0:subset_size])
yield cur / divisor
for x in range(subset_size, len(data)):
cur += data[x] - data[x - subset_size]
yield cur / divisor
else:
# Based on the recipe at:
# http://docs.python.org/library/collections.html#deque-recipes
it = iter(data)
d = deque(islice(it, subset_size))
if subset_size > len(d):
raise ValueError('subset_size must be smaller than data set size')
if avoid_fp_drift:
yield sum(d) / divisor
for elem in it:
d.popleft()
d.append(elem)
yield sum(d) / divisor
else:
s = sum(d)
yield s / divisor
for elem in it:
s += elem - d.popleft()
d.append(elem)
yield s / divisor
##########################
if __name__ == '__main__':
import unittest
class TestMovingAverage(unittest.TestCase):
####################
def test_List(self):
try:
list(movingaverage([1,2,3], 0))
self.fail('Did not raise ValueError on subset_size=0')
except ValueError:
pass
try:
list(movingaverage([1,2,3,4,5,6], 7))
self.fail('Did not raise ValueError on subset_size > len(data)')
except ValueError:
pass
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1)), [1,2,3,4,5,6])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2)),
[1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2)),
[1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3)), [2,3,4,5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4)), [2.5,3.5,4.5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5)), [3,4])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6)), [3.5])
self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
3, False)), [40.0,42.0,45.0,43.0])
self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
3, True)), [40.0,42.0,45.0,43.0])
######################
def test_XRange(self):
try:
list(movingaverage(xrange(1, 4), 0))
self.fail('Did not raise ValueError on subset_size=0')
except ValueError:
pass
try:
list(movingaverage(xrange(1, 7), 7))
self.fail('Did not raise ValueError on subset_size > len(data)')
except ValueError:
pass
self.assertEqual(list(movingaverage(xrange(1, 7), 1)), [1,2,3,4,5,6])
self.assertEqual(list(movingaverage(xrange(1, 7), 2)),
[1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
2)), [1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(xrange(1, 7), 3)), [2,3,4,5])
self.assertEqual(list(movingaverage(xrange(1, 7), 4)), [2.5,3.5,4.5])
self.assertEqual(list(movingaverage(xrange(1, 7), 5)), [3,4])
self.assertEqual(list(movingaverage(xrange(1, 7), 6)), [3.5])
###########################
def test_ListRolling(self):
try:
list(movingaverage([1,2,3], 0, avoid_fp_drift = False))
self.fail('Did not raise ValueError on subset_size=0')
except ValueError:
pass
try:
list(movingaverage([1,2,3,4,5,6], 7, avoid_fp_drift = False))
self.fail('Did not raise ValueError on subset_size > len(data)')
except ValueError:
pass
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 1,
avoid_fp_drift = False)), [1,2,3,4,5,6])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 2,
avoid_fp_drift = False)),
[1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(map(float, [1,2,3,4,5,6]), 2,
avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 3,
avoid_fp_drift = False)), [2,3,4,5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 4,
avoid_fp_drift = False)), [2.5,3.5,4.5])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 5,
avoid_fp_drift = False)), [3,4])
self.assertEqual(list(movingaverage([1,2,3,4,5,6], 6,
avoid_fp_drift = False)), [3.5])
self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
3, False, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
self.assertEqual(list(movingaverage([40, 30, 50, 46, 39, 44],
3, True, avoid_fp_drift = False)), [40.0,42.0,45.0,43.0])
#############################
def test_XRangeRolling(self):
try:
list(movingaverage(xrange(1, 4), 0, avoid_fp_drift = False))
self.fail('Did not raise ValueError on subset_size=0')
except ValueError:
pass
try:
list(movingaverage(xrange(1, 7), 7, avoid_fp_drift = False))
self.fail('Did not raise ValueError on subset_size > len(data)')
except ValueError:
pass
self.assertEqual(list(movingaverage(xrange(1, 7), 1,
avoid_fp_drift = False)), [1,2,3,4,5,6])
self.assertEqual(list(movingaverage(xrange(1, 7), 2,
avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(iter(map(float, xrange(1, 7))),
2, avoid_fp_drift = False)), [1.5,2.5,3.5,4.5,5.5])
self.assertEqual(list(movingaverage(xrange(1, 7), 3,
avoid_fp_drift = False)), [2,3,4,5])
self.assertEqual(list(movingaverage(xrange(1, 7), 4,
avoid_fp_drift = False)), [2.5,3.5,4.5])
self.assertEqual(list(movingaverage(xrange(1, 7), 5,
avoid_fp_drift = False)), [3,4])
self.assertEqual(list(movingaverage(xrange(1, 7), 6,
avoid_fp_drift = False)), [3.5])
######################################################################
suite = unittest.TestLoader().loadTestsFromTestCase(TestMovingAverage)
unittest.TextTestRunner(verbosity = 2).run(suite)
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