def test_avg_is_nan_for_empty_input(self):
computedStats = statistics.calculateStats([])
# All fields of computedStats (average, max, min) must be
# nan (not-a-number), as defined in the math package
# Design the assert here.
# Use nan and isnan in https://docs.python.org/3/library/math.html
self.assertTrue(computedStats["avg"], math.nan)
def test_avg_is_nan_for_empty_input(self):
computedStats = statistics.calculateStats([])
# All fields of computedStats (average, max, min) must be
# nan (not-a-number), as defined in the math package
# Design the assert here.
self.assertTrue(math.isnan(computedStats["avg"]))
self.assertTrue(math.isnan(computedStats["max"]))
self.assertTrue(math.isnan(computedStats["min"]))
def test_avg_is_nan_for_empty_input(self): computedStats = statistics.calculateStats([]) # All fields of computedStats (average, max, min) must be # nan (not-a-number), as defined in the math package # Design the assert here. self.assertIs(computedStats["avg"], math.nan, 'mean requires at least one data point') self.assertIs(computedStats["max"], math.nan) self.assertIs(computedStats["min"], math.nan)
def test_report_min_max_avg(self):
computedStats = statistics.calculateStats([1.5, 8.9, 3.2, 4.5])
epsilon = 0.001
self.assertAlmostEqual(computedStats["avg"], 4.525, delta=epsilon)
self.assertAlmostEqual(computedStats["max"], 8.9, delta=epsilon)
self.assertAlmostEqual(computedStats["min"], 1.5, delta=epsilon)
def test_avg_is_nan_for_empty_input(self):
computedStats = statistics.calculateStats([])
# print('max1:',computedStats["max"])
self.assertTrue(math.isnan(computedStats["avg"]))
self.assertTrue(math.isnan(computedStats["max"]))
self.assertTrue(math.isnan(computedStats["min"]))
def test_avg_is_nan_for_empty_input(self): computedStats = statistics.calculateStats([])
def checkAndAlert(self, numbers):
computedStats = statistics.calculateStats(numbers)
if computedStats["max"] > self.maxThreshold:
for obj in self.alertObjects:
obj.trigger()
