def construct_summary(self, vals, testname):
if testname.startswith('raptor-v8_7'):
return self.v8_Metric(vals)
elif testname.startswith('raptor-kraken'):
return self.JS_Metric(vals)
elif testname.startswith('raptor-jetstream'):
return self.benchmark_score(vals)
elif testname.startswith('raptor-speedometer'):
return self.speedometer_score(vals)
elif testname.startswith('raptor-stylebench'):
return self.stylebench_score(vals)
elif testname.startswith('raptor-sunspider'):
return self.sunspider_score(vals)
elif testname.startswith('raptor-unity-webgl'):
return self.unity_webgl_score(vals)
elif testname.startswith('raptor-webaudio'):
return self.webaudio_score(vals)
elif testname.startswith('raptor-assorted-dom'):
return self.assorted_dom_score(vals)
elif testname.startswith('raptor-wasm-misc'):
return self.wasm_misc_score(vals)
elif testname.startswith('raptor-wasm-godot'):
return self.wasm_godot_score(vals)
elif testname.startswith('raptor-youtube-playback'):
return self.youtube_playback_performance_score(vals)
elif testname.startswith('supporting_data'):
return self.supporting_data_total(vals)
elif len(vals) > 1:
return round(filters.geometric_mean([i for i, j in vals]), 2)
else:
return round(filters.mean([i for i, j in vals]), 2)
def parseSunspiderOutput(self, test):
_subtests = {}
data = test.measurements['sunspider']
for page_cycle in data:
for sub, replicates in page_cycle[0].iteritems():
# for each pagecycle, build a list of subtests and append all related replicates
if sub not in _subtests.keys():
# subtest not added yet, first pagecycle, so add new one
_subtests[sub] = {
'unit': test.subtest_unit,
'alertThreshold': float(test.alert_threshold),
'lowerIsBetter': test.subtest_lower_is_better,
'name': sub,
'replicates': []
}
_subtests[sub]['replicates'].extend(
[round(x, 3) for x in replicates])
subtests = []
vals = []
names = _subtests.keys()
names.sort(reverse=True)
for name in names:
_subtests[name]['value'] = filters.mean(
_subtests[name]['replicates'])
subtests.append(_subtests[name])
vals.append([_subtests[name]['value'], name])
return subtests, vals
def youtube_playback_performance_score(cls, val_list):
"""Calculate percentage of failed tests."""
results = [i for i, j in val_list]
return round(filters.mean(results), 2)
def wasm_godot_score(cls, val_list):
"""
wasm_godot_score: first-interactive mean
"""
results = [i for i, j in val_list if j == 'first-interactive']
return filters.mean(results)
def wasm_misc_score(cls, val_list):
"""
wasm_misc_score: self reported as '__total__'
"""
results = [i for i, j in val_list if j == '__total__']
return filters.mean(results)
def unity_webgl_score(cls, val_list):
"""
unity_webgl_score: self reported as 'Geometric Mean'
"""
results = [i for i, j in val_list if j == 'Geometric Mean']
return filters.mean(results)
def benchmark_score(cls, val_list):
"""
benchmark_score: ares6/jetstream self reported as 'geomean'
"""
results = [i for i, j in val_list if j == 'geomean']
return filters.mean(results)
