From 992c3cdb7de6a52ee95fec34e0f0442c7d91e5ad Mon Sep 17 00:00:00 2001 From: Henrik Skupin Date: Fri, 17 May 2019 11:12:40 +0000 Subject: [PATCH] Bug 1548845 - [raptor] Fix local import of filter module. r=perftest-reviewers,rwood To not collide with the built-in "filter" method, the local filter module should be named as filters. Differential Revision: https://phabricator.services.mozilla.com/D30532 --HG-- rename : testing/raptor/raptor/filter.py => testing/raptor/raptor/filters.py extra : moz-landing-system : lando --- .../raptor/raptor/{filter.py => filters.py} | 6 +-- testing/raptor/raptor/output.py | 48 +++++++++---------- 2 files changed, 27 insertions(+), 27 deletions(-) rename testing/raptor/raptor/{filter.py => filters.py} (97%) diff --git a/testing/raptor/raptor/filter.py b/testing/raptor/raptor/filters.py similarity index 97% rename from testing/raptor/raptor/filter.py rename to testing/raptor/raptor/filters.py index 33378dcbd222..b5f06d80a061 100644 --- a/testing/raptor/raptor/filter.py +++ b/testing/raptor/raptor/filters.py @@ -16,10 +16,10 @@ 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 raptor import filter - filters = filter.ignore_first.prepare(1) + filter.median.prepare() + from raptor import filters + filter_list = filters.ignore_first.prepare(1) + filters.median.prepare() - for filter in filters: + for filter in filter_list: data = filter(data) # data is filtered """ diff --git a/testing/raptor/raptor/output.py b/testing/raptor/raptor/output.py index 108901de4530..078056a56e61 100644 --- a/testing/raptor/raptor/output.py +++ b/testing/raptor/raptor/output.py @@ -8,7 +8,7 @@ """output raptor test results""" from __future__ import absolute_import -import filter +import filters import json import os @@ -101,7 +101,7 @@ class Output(object): # for warm page-load, ignore first value due to 1st pageload noise LOG.info("ignoring the first %s value due to initial pageload noise" % measurement_name) - filtered_values = filter.ignore_first(new_subtest['replicates'], 1) + filtered_values = filters.ignore_first(new_subtest['replicates'], 1) else: # for cold-load we want all the values filtered_values = new_subtest['replicates'] @@ -111,7 +111,7 @@ class Output(object): # cases where TTFI is not available, which is acceptable; however we don't want # to include those '-1' TTFI values in our final results calculations if measurement_name == "ttfi": - filtered_values = filter.ignore_negative(filtered_values) + filtered_values = filters.ignore_negative(filtered_values) # we've already removed the first pageload value; if there aren't any more # valid TTFI values available for this pageload just remove it from results if len(filtered_values) < 1: @@ -125,7 +125,7 @@ class Output(object): % measurement_name) new_subtest['shouldAlert'] = True - new_subtest['value'] = filter.median(filtered_values) + new_subtest['value'] = filters.median(filtered_values) vals.append([new_subtest['value'], new_subtest['name']]) subtests.append(new_subtest) @@ -272,7 +272,7 @@ class Output(object): vals = [] for next_sub in combined_suites[name]['subtests']: # calculate sub-test results (i.e. each measurement type) - next_sub['value'] = filter.median(next_sub['replicates']) + next_sub['value'] = filters.median(next_sub['replicates']) # add to vals; vals is used to calculate overall suite result i.e. the # geomean of all of the subtests / measurement types vals.append([next_sub['value'], next_sub['name']]) @@ -404,7 +404,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -441,7 +441,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -480,7 +480,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -527,7 +527,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -582,7 +582,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -609,7 +609,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.mean(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.mean(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -654,7 +654,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = filter.median(_subtests[name]['replicates']) + _subtests[name]['value'] = filters.median(_subtests[name]['replicates']) subtests.append(_subtests[name]) vals.append([_subtests[name]['value'], name]) @@ -693,7 +693,7 @@ class Output(object): names = _subtests.keys() names.sort(reverse=True) for name in names: - _subtests[name]['value'] = round(filter.median(_subtests[name]['replicates']), 2) + _subtests[name]['value'] = round(filters.median(_subtests[name]['replicates']), 2) subtests.append(_subtests[name]) # only use the 'total's to compute the overall result if name == 'total': @@ -830,7 +830,7 @@ class Output(object): @classmethod def v8_Metric(cls, val_list): results = [i for i, j in val_list] - score = 100 * filter.geometric_mean(results) + score = 100 * filters.geometric_mean(results) return score @classmethod @@ -853,7 +853,7 @@ class Output(object): raise Exception("Speedometer has 160 subtests, found: %s instead" % len(results)) results = results[9::10] - score = 60 * 1000 / filter.geometric_mean(results) / correctionFactor + score = 60 * 1000 / filters.geometric_mean(results) / correctionFactor return score @classmethod @@ -862,7 +862,7 @@ class Output(object): benchmark_score: ares6/jetstream self reported as 'geomean' """ results = [i for i, j in val_list if j == 'geomean'] - return filter.mean(results) + return filters.mean(results) @classmethod def webaudio_score(cls, val_list): @@ -870,7 +870,7 @@ class Output(object): webaudio_score: self reported as 'Geometric Mean' """ results = [i for i, j in val_list if j == 'Geometric Mean'] - return filter.mean(results) + return filters.mean(results) @classmethod def unity_webgl_score(cls, val_list): @@ -878,7 +878,7 @@ class Output(object): unity_webgl_score: self reported as 'Geometric Mean' """ results = [i for i, j in val_list if j == 'Geometric Mean'] - return filter.mean(results) + return filters.mean(results) @classmethod def wasm_misc_score(cls, val_list): @@ -886,7 +886,7 @@ class Output(object): wasm_misc_score: self reported as '__total__' """ results = [i for i, j in val_list if j == '__total__'] - return filter.mean(results) + return filters.mean(results) @classmethod def wasm_godot_score(cls, val_list): @@ -894,7 +894,7 @@ class Output(object): wasm_godot_score: first-interactive mean """ results = [i for i, j in val_list if j == 'first-interactive'] - return filter.mean(results) + return filters.mean(results) @classmethod def stylebench_score(cls, val_list): @@ -940,7 +940,7 @@ class Output(object): raise Exception("StyleBench has 380 entries, found: %s instead" % len(results)) results = results[75::76] - score = 60 * 1000 / filter.geometric_mean(results) / correctionFactor + score = 60 * 1000 / filters.geometric_mean(results) / correctionFactor return score @classmethod @@ -951,7 +951,7 @@ class Output(object): @classmethod def assorted_dom_score(cls, val_list): results = [i for i, j in val_list] - return round(filter.geometric_mean(results), 2) + return round(filters.geometric_mean(results), 2) @classmethod def supporting_data_total(cls, val_list): @@ -984,6 +984,6 @@ class Output(object): elif testname.startswith('supporting_data'): return self.supporting_data_total(vals) elif len(vals) > 1: - return round(filter.geometric_mean([i for i, j in vals]), 2) + return round(filters.geometric_mean([i for i, j in vals]), 2) else: - return round(filter.mean([i for i, j in vals]), 2) + return round(filters.mean([i for i, j in vals]), 2)