1 #!/usr/bin/env python 2 3 # Copyright (C) 2007, 2012 Apple Inc. All rights reserved. 4 # 5 # Redistribution and use in source and binary forms, with or without 6 # modification, are permitted provided that the following conditions 7 # are met: 8 # 1. Redistributions of source code must retain the above copyright 9 # notice, this list of conditions and the following disclaimer. 10 # 2. Redistributions in binary form must reproduce the above copyright 11 # notice, this list of conditions and the following disclaimer in the 12 # documentation and/or other materials provided with the distribution. 13 # 14 # THIS SOFTWARE IS PROVIDED BY APPLE COMPUTER, INC. ``AS IS'' AND ANY 15 # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 16 # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 17 # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE COMPUTER, INC. OR 18 # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 19 # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 20 # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 21 # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY 22 # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 23 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 24 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 25 26 import math 27 import sys 28 import re 29 import fileinput 30 from optparse import OptionParser 31 32 usage = "usage: %prog [options] [FILES]\n Compute the mean and 95% confidence interval of a sample set.\n Standard input or files must contain two or more decimal numbers, one per line." 33 parser = OptionParser(usage=usage) 34 parser.add_option("-u", "--unit", dest="unit", default="", 35 help="assume values are in units of UNIT", metavar="UNIT") 36 parser.add_option("-v", "--verbose", 37 action="store_true", dest="verbose", default=False, 38 help="print all values (with units)") 39 (options, files) = parser.parse_args() 40 41 def sum(items): 42 return reduce(lambda x,y: x+y, items) 43 44 def arithmeticMean(items): 45 return sum(items) / len(items) 46 47 def standardDeviation(mean, items): 48 deltaSquares = [(item - mean) ** 2 for item in items] 49 return math.sqrt(sum(deltaSquares) / (len(items) - 1)) 50 51 def standardError(stdDev, items): 52 return stdDev / math.sqrt(len(items)) 53 54 # t-distribution for 2-sided 95% confidence intervals 55 tDistribution = [float('NaN'), float('NaN'), 12.71, 4.30, 3.18, 2.78, 2.57, 2.45, 2.36, 2.31, 2.26, 2.23, 2.20, 2.18, 2.16, 2.14, 2.13, 2.12, 2.11, 2.10, 2.09, 2.09, 2.08, 2.07, 2.07, 2.06, 2.06, 2.06, 2.05, 2.05, 2.05, 2.04, 2.04, 2.04, 2.03, 2.03, 2.03, 2.03, 2.03, 2.02, 2.02, 2.02, 2.02, 2.02, 2.02, 2.02, 2.01, 2.01, 2.01, 2.01, 2.01, 2.01, 2.01, 2.01, 2.01, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 2.00, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.99, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.98, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.97, 1.96] 56 tMax = len(tDistribution) 57 tLimit = 1.96 58 59 def tDist(n): 60 if n > tMax: 61 return tLimit 62 return tDistribution[n] 63 64 def twoSidedConfidenceInterval(items): 65 mean = arithmeticMean(items) 66 stdDev = standardDeviation(mean, items) 67 stdErr = standardError(stdDev, items) 68 return tDist(len(items)) * stdErr 69 70 results = [] 71 72 decimalNumberPattern = re.compile(r"\d+\.?\d*") 73 for line in fileinput.input(files): 74 match = re.search(decimalNumberPattern, line) 75 if match: 76 results.append(float(match.group(0))) 77 78 if len(results) == 0: 79 parser.print_help() 80 quit() 81 82 83 mean = arithmeticMean(results) 84 confidenceInterval = twoSidedConfidenceInterval(results) 85 confidencePercent = 100 * confidenceInterval / mean 86 87 if options.verbose: 88 length = 7 89 for item in results: 90 line = " %.2f %s" % (item, options.unit) 91 print line 92 length = len(line) if len(line) > length else length 93 94 print "-" * length 95 96 prefix = "Mean: " if options.verbose else "" 97 print "%s%.2f %s +/- %.2f %s (%.1f%%)" % (prefix, mean, options.unit, confidenceInterval, options.unit, confidencePercent) 98 99
