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Diffstat (limited to 'testing/talos/talos/filter.py')
| -rw-r--r-- | testing/talos/talos/filter.py | 264 |
1 files changed, 264 insertions, 0 deletions
diff --git a/testing/talos/talos/filter.py b/testing/talos/talos/filter.py new file mode 100644 index 0000000000..833553139f --- /dev/null +++ b/testing/talos/talos/filter.py @@ -0,0 +1,264 @@ +# This Source Code Form is subject to the terms of the Mozilla Public +# License, v. 2.0. If a copy of the MPL was not distributed with this +# file, You can obtain one at http://mozilla.org/MPL/2.0/. + +import math + +import six + +""" +data filters: +takes a series of run data and applies statistical transforms to it + +Each filter is a simple function, but it also have attached a special +`prepare` method that create a tuple with one instance of a +:class:`Filter`; this allow to write stuff like:: + + from talos import filter + filters = filter.ignore_first.prepare(1) + filter.median.prepare() + + for filter in filters: + data = filter(data) + # data is filtered +""" + +_FILTERS = {} + + +class Filter(object): + def __init__(self, func, *args, **kwargs): + """ + Takes a filter function, and save args and kwargs that + should be used when the filter is used. + """ + self.func = func + self.args = args + self.kwargs = kwargs + + def apply(self, data): + """ + Apply the filter on the data, and return the new data + """ + return self.func(data, *self.args, **self.kwargs) + + +def define_filter(func): + """ + decorator to attach the prepare method. + """ + + def prepare(*args, **kwargs): + return (Filter(func, *args, **kwargs),) + + func.prepare = prepare + return func + + +def register_filter(func): + """ + all filters defined in this module + should be registered + """ + global _FILTERS + + _FILTERS[func.__name__] = func + return func + + +def filters(*args): + global _FILTERS + + filters_ = [_FILTERS[filter] for filter in args] + return filters_ + + +def apply(data, filters): + for filter in filters: + data = filter(data) + + return data + + +def parse(string_): + def to_number(string_number): + try: + return int(string_number) + except ValueError: + return float(string_number) + + tokens = string_.split(":") + + func = tokens[0] + digits = [] + if len(tokens) > 1: + digits.extend(tokens[1].split(",")) + digits = [to_number(digit) for digit in digits] + + return [func, digits] + + +# filters that return a scalar + + +@register_filter +@define_filter +def mean(series): + """ + mean of data; needs at least one data point + """ + return sum(series) / float(len(series)) + + +@register_filter +@define_filter +def median(series): + """ + median of data; needs at least one data point + """ + series = sorted(series) + if len(series) % 2: + # odd + return series[int(len(series) / 2)] + else: + # even + middle = int(len(series) / 2) # the higher of the middle 2, actually + return 0.5 * (series[middle - 1] + series[middle]) + + +@register_filter +@define_filter +def variance(series): + """ + variance: http://en.wikipedia.org/wiki/Variance + """ + + _mean = mean(series) + variance = sum([(i - _mean) ** 2 for i in series]) / float(len(series)) + return variance + + +@register_filter +@define_filter +def stddev(series): + """ + standard deviation: http://en.wikipedia.org/wiki/Standard_deviation + """ + return variance(series) ** 0.5 + + +@register_filter +@define_filter +def dromaeo(series): + """ + dromaeo: https://wiki.mozilla.org/Dromaeo, pull the internal calculation + out + * This is for 'runs/s' based tests, not 'ms' tests. + * chunksize: defined in dromaeo: tests/dromaeo/webrunner.js#l8 + """ + means = [] + chunksize = 5 + series = list(dromaeo_chunks(series, chunksize)) + for i in series: + means.append(mean(i)) + return geometric_mean(means) + + +@register_filter +@define_filter +def dromaeo_chunks(series, size): + for i in six.moves.range(0, len(series), size): + yield series[i : i + size] + + +@register_filter +@define_filter +def geometric_mean(series): + """ + geometric_mean: http://en.wikipedia.org/wiki/Geometric_mean + """ + total = 0 + for i in series: + total += math.log(i + 1) + # pylint --py3k W1619 + return math.exp(total / len(series)) - 1 + + +# filters that return a list + + +@register_filter +@define_filter +def ignore_first(series, number=1): + """ + ignore first datapoint + """ + if len(series) <= number: + # don't modify short series + return series + return series[number:] + + +@register_filter +@define_filter +def ignore(series, function): + """ + ignore the first value of a list given by function + """ + if len(series) <= 1: + # don't modify short series + return series + series = series[:] # do not mutate the original series + value = function(series) + series.remove(value) + return series + + +@register_filter +@define_filter +def ignore_max(series): + """ + ignore maximum data point + """ + return ignore(series, max) + + +@register_filter +@define_filter +def ignore_min(series): + """ + ignore minimum data point + """ + return ignore(series, min) + + +@register_filter +@define_filter +def v8_subtest(series, name): + """ + v8 benchmark score - modified for no sub benchmarks. + * removed Crypto and kept Encrypt/Decrypt standalone + * removed EarlyBoyer and kept Earley/Boyer standalone + + this is not 100% in parity but within .3% + """ + reference = { + "Encrypt": 266181.0, + "Decrypt": 266181.0, + "DeltaBlue": 66118.0, + "Earley": 666463.0, + "Boyer": 666463.0, + "NavierStokes": 1484000.0, + "RayTrace": 739989.0, + "RegExp": 910985.0, + "Richards": 35302.0, + "Splay": 81491.0, + } + + # pylint --py3k W1619 + return reference[name] / geometric_mean(series) + + +@register_filter +@define_filter +def responsiveness_Metric(val_list): + return sum([float(x) * float(x) / 1000000.0 for x in val_list]) |