本文整理汇总了Python中webkitpy.performance_tests.perftest.PerfTest.compute_statistics方法的典型用法代码示例。如果您正苦于以下问题：Python PerfTest.compute_statistics方法的具体用法？Python PerfTest.compute_statistics怎么用？Python PerfTest.compute_statistics使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类webkitpy.performance_tests.perftest.PerfTest的用法示例。
在下文中一共展示了PerfTest.compute_statistics方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_compute_statistics
# 需要导入模块: from webkitpy.performance_tests.perftest import PerfTest [as 别名]
# 或者: from webkitpy.performance_tests.perftest.PerfTest import compute_statistics [as 别名]
def test_compute_statistics(self):
def compute_statistics(values):
statistics = PerfTest.compute_statistics(map(lambda x: float(x), values))
return json.loads(json.dumps(statistics))
statistics = compute_statistics([10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11])
self.assertEqual(sorted(statistics.keys()), ['avg', 'max', 'median', 'min', 'stdev'])
self.assertEqual(statistics['avg'], 10.5)
self.assertEqual(statistics['min'], 1)
self.assertEqual(statistics['max'], 20)
self.assertEqual(statistics['median'], 10.5)
self.assertEqual(compute_statistics([8, 9, 10, 11, 12])['avg'], 10)
self.assertEqual(compute_statistics([8, 9, 10, 11, 12] * 4)['avg'], 10)
self.assertEqual(compute_statistics([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])['avg'], 10)
self.assertEqual(PerfTest.compute_statistics([1, 5, 2, 8, 7])['median'], 5)
self.assertEqual(PerfTest.compute_statistics([1, 6, 2, 8, 7, 2])['median'], 4)
self.assertAlmostEqual(statistics['stdev'], math.sqrt(35))
self.assertAlmostEqual(compute_statistics([1, 2, 3, 4, 5, 6])['stdev'], math.sqrt(3.5))
self.assertAlmostEqual(compute_statistics([4, 2, 5, 8, 6])['stdev'], math.sqrt(5))示例2: compute_statistics
# 需要导入模块: from webkitpy.performance_tests.perftest import PerfTest [as 别名]
# 或者: from webkitpy.performance_tests.perftest.PerfTest import compute_statistics [as 别名]
def compute_statistics(values):
statistics = PerfTest.compute_statistics(map(lambda x: float(x), values))
return json.loads(json.dumps(statistics))