Tools/Scripts/sampstat - Issue 1253013003: Remove all perl scripts from Tools/Scripts

Unified Diff: Tools/Scripts/sampstat

Issue 1253013003: Remove all perl scripts from Tools/Scripts (Closed) Base URL: svn://svn.chromium.org/blink/trunk

Patch Set: Remove the python code to invoke Perl \o/ Created 5 years, 5 months ago

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Index: Tools/Scripts/sampstat

diff --git a/Tools/Scripts/sampstat b/Tools/Scripts/sampstat

deleted file mode 100755

index b8a2ca9ff1b71bad455e8af784af30fb0a8457c1..0000000000000000000000000000000000000000

--- a/Tools/Scripts/sampstat

+++ /dev/null

@@ -1,98 +0,0 @@

-#!/usr/bin/env python

-# Redistribution and use in source and binary forms, with or without

-# modification, are permitted provided that the following conditions

-# are met:

-# 1. Redistributions of source code must retain the above copyright

-# notice, this list of conditions and the following disclaimer.

-# 2. Redistributions in binary form must reproduce the above copyright

-# notice, this list of conditions and the following disclaimer in the

-# documentation and/or other materials provided with the distribution.

-# THIS SOFTWARE IS PROVIDED BY APPLE COMPUTER, INC. ``AS IS'' AND ANY

-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE

-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR

-# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE COMPUTER, INC. OR

-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,

-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,

-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR

-# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY

-# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT

-# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

-import math

-import sys

-import re

-import fileinput

-from optparse import OptionParser

-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."

-parser = OptionParser(usage=usage)

-parser.add_option("-u", "--unit", dest="unit", default="",

- help="assume values are in units of UNIT", metavar="UNIT")

-parser.add_option("-v", "--verbose",

- action="store_true", dest="verbose", default=False,

- help="print all values (with units)")

-(options, files) = parser.parse_args()

-def sum(items):

- return reduce(lambda x,y: x+y, items)

-def arithmeticMean(items):

- return sum(items) / len(items)

-def standardDeviation(mean, items):

- deltaSquares = [(item - mean) ** 2 for item in items]

- return math.sqrt(sum(deltaSquares) / (len(items) - 1))

-def standardError(stdDev, items):

- return stdDev / math.sqrt(len(items))

-# t-distribution for 2-sided 95% confidence intervals

-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]

-tMax = len(tDistribution)

-tLimit = 1.96

-def tDist(n):

- if n > tMax:

- return tLimit

- return tDistribution[n]

-def twoSidedConfidenceInterval(items):

- mean = arithmeticMean(items)

- stdDev = standardDeviation(mean, items)

- stdErr = standardError(stdDev, items)

- return tDist(len(items)) * stdErr

-results = []

-decimalNumberPattern = re.compile(r"\d+\.?\d*")

-for line in fileinput.input(files):

- match = re.search(decimalNumberPattern, line)

- if match:

- results.append(float(match.group(0)))

-if len(results) == 0:

- parser.print_help()

- quit()

-mean = arithmeticMean(results)

-confidenceInterval = twoSidedConfidenceInterval(results)

-confidencePercent = 100 * confidenceInterval / mean

-if options.verbose:

- length = 7

- for item in results:

- line = " %.2f %s" % (item, options.unit)

- print line

- length = len(line) if len(line) > length else length

- print "-" * length

-prefix = "Mean: " if options.verbose else ""

-print "%s%.2f %s +/- %.2f %s (%.1f%%)" % (prefix, mean, options.unit, confidenceInterval, options.unit, confidencePercent)

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