Reorganize profiling scripts.

1 files changed, 209 insertions, 0 deletions

diff --git a/scripts/profiling/movingaverage.py b/scripts/profiling/movingaverage.py
new file mode 100644
index 00000000..bac1b3e1
--- /dev/null
+++ b/scripts/profiling/movingaverage.py
@@ -0,0 +1,209 @@
+#!/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)
+