Moved chrome autotests to chromium depot

client/site_tests/desktopui_PageCyclerTests/page_cycler_results_parser.py

Index: client/site_tests/desktopui_PageCyclerTests/page_cycler_results_parser.py
diff --git a/client/site_tests/desktopui_PageCyclerTests/page_cycler_results_parser.py b/client/site_tests/desktopui_PageCyclerTests/page_cycler_results_parser.py
deleted file mode 100644
index 1d8e15a3719df4e5352771d85c56fa6297d9c8ff..0000000000000000000000000000000000000000
--- a/client/site_tests/desktopui_PageCyclerTests/page_cycler_results_parser.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import subprocess as sub
-import re
-import sys
-import math, functools
-
-class SiteTimes(object):
-    def __init__(self):
-        self.site='UNDEFINED'
-        self.times=[]
-
-# helper function found online here:
-# http://code.activestate.com/recipes/511478-finding-the-percentile-
-# of-the-values/
-def percentile(N, percent, key=lambda x:x):
-    """
-    Find the percentile of a list of values.
-
-    @parameter N - is a list of values. Note N MUST BE already sorted.
-    @parameter percent - a float value from 0.0 to 1.0.
-    @parameter key - optional key function to compute value from each 
-    element of N.
-
-    @return - the percentile of the values
-    """
-    if not N:
-        return None
-    k = (len(N)-1) * percent
-    f = math.floor(k)
-    c = math.ceil(k)
-    if f == c:
-        return key(N[int(k)])
-    d0 = key(N[int(f)]) * (k-f)
-    d1 = key(N[int(c)]) * (c-k)
-    return d0+d1
-
-def mean(numbers):
-    assert(len(numbers) != 0), 'list should not be empty!'
-    return sum(numbers)/len(numbers)
-
-class PageCyclerResultsParser:
-    def parse_file(self, outfile = 'out.txt'):
-        # output is the output of the page_cycler tests.
-        output = open(outfile).read()
-        return self.parse_results(output)
-
-    def parse_results(self, output = ''):
-        # median is 50th percentile.
-        median = functools.partial(percentile, percent=0.5)
-
-        assert(output != ''), 'Output cannot be empty!'
-
-        # split it up into lines
-        lines = output.split('\n')
-
-        # figure out where the results are...
-        found = False
-        # This is our anchor in the text
-        token = '*RESULT times:'
-        for index, line in enumerate(lines):
-            if(line.startswith(token)):
-                found = True
-                break
-
-        assert(found==True), token+' not found!?'
-        timesline = lines[index]
-        sitesline = lines[index-1]
-
-        # we have a line called times and a line called sites
-        m = re.search('\[(.*?)\]', sitesline)
-        sites = m.group(1).split(',')
-
-        m = re.search('\[(.*?)\]', timesline)
-        times = m.group(1).split(',')
-
-        assert(len(times) % len(sites) == 0), 'Times not divisible by sites!'
-
-        iterations = len(times)/len(sites)
-
-        # now we have a list called sites and a list called times
-        # let's do some statistics on it.
-        stList = []
-
-        # go over all the sites and populate the stlist data structure
-        for ii, site in enumerate(sites):
-            st = SiteTimes()
-            st.site = site
-            for jj in range(0, iterations):
-                mytime = float(times[jj*len(sites)+ii])
-                st.times.append(mytime)
-            stList.append(st)
-
-        # For debugging use something like this:
-        ###for ii, st in enumerate(stList):
-        ###  print st.site
-        ###  print st.times
-
-        # now remove the lowest element and print out mean of medians
-        medianList = []
-
-        totalTime = 0
-        for ii, st in enumerate(stList):
-            sortedTimes=sorted(st.times)
-            # drop highest time in the sortedTimes
-            sortedTimes.pop()
-            # TODO: Perhaps this should be a weighted mean?
-            totalTime += mean(sortedTimes)
-
-        return totalTime/len(stList)
-
-# This is how to use this class
-###pcrp=PageCyclerResultsParser()
-###print pcrp.parse_file('out.txt')