Module quantum_inferno.tests.utilities.test_sampling
Classes
class TestSampling (methodName='runTest')-
A class whose instances are single test cases.
By default, the test code itself should be placed in a method named 'runTest'.
If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.
When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.
Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.
Expand source code
class TestSampling(unittest.TestCase): @classmethod def setUpClass(cls) -> None: cls.timeseries = np.array([10, 20, 30, 40, 50, 60, 70, 80, 70, 60, 50, 40, 30, 20]) def test_subsample(self): result, new_rate = samp.subsample(self.timeseries, 1.0, 3, "nth") self.assertEqual(len(result), 5) self.assertEqual(result[0], 10) self.assertEqual(result[-1], 30) self.assertAlmostEqual(new_rate, 0.33, 2) def test_subsample_avg(self): result, new_rate = samp.subsample(self.timeseries, 1.0, 3, "average") self.assertEqual(len(result), 4) self.assertEqual(result[0], 20) self.assertEqual(result[-1], 50) self.assertAlmostEqual(new_rate, 0.33, 2) def test_subsample_med(self): result, new_rate = samp.subsample(self.timeseries, 1.0, 3, "median") self.assertEqual(len(result), 4) self.assertEqual(result[0], 20) self.assertEqual(result[-1], 50) self.assertAlmostEqual(new_rate, 0.33, 2) def test_subsample_max(self): result, new_rate = samp.subsample(self.timeseries, 1.0, 3, "max") self.assertEqual(len(result), 4) self.assertEqual(result[0], 30) self.assertEqual(result[-1], 60) self.assertAlmostEqual(new_rate, 0.33, 2) def test_subsample_min(self): result, new_rate = samp.subsample(self.timeseries, 1.0, 3, "min") self.assertEqual(len(result), 4) self.assertEqual(result[0], 10) self.assertEqual(result[-1], 40) self.assertAlmostEqual(new_rate, 0.33, 2) def test_resample_uneven_timestamps(self): timestamps = np.array([10, 15, 22, 31, 41, 46, 53, 60, 75, 79, 89, 92, 93, 100]) result, new_rate = samp.resample_uneven_timeseries(self.timeseries, timestamps, 0.2) self.assertEqual(len(result), 18) self.assertEqual(result[0], 10) self.assertAlmostEqual(result[-1], 27.14, 2) self.assertAlmostEqual(result[4], 38.89, 2) self.assertEqual(new_rate, 0.2) def test_resample_uneven_timestamps_assumed(self): timestamps = np.array([10, 15, 22, 31, 41, 46, 53, 60, 75, 79, 89, 92, 93, 100]) result, new_rate = samp.resample_uneven_timeseries(self.timeseries, timestamps) self.assertEqual(len(result), 13) self.assertEqual(result[0], 10) self.assertAlmostEqual(result[-1], 29.89, 2) self.assertAlmostEqual(result[4], 46.69, 2) self.assertAlmostEqual(new_rate, 0.14, 2) def test_resample_with_sample_rate(self): result, new_rate = samp.resample_with_sample_rate(self.timeseries, 1.0, 0.5) self.assertEqual(len(result), 7) self.assertAlmostEqual(result[0], 12.47, 2) self.assertAlmostEqual(result[-1], 30.32, 2) self.assertAlmostEqual(new_rate, 0.5, 2) def test_subsample_2d(self): array = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) result = samp.subsample_2d(array, 2, "nth") self.assertEqual(result.shape, (3, 2)) self.assertEqual(result[0][0], 1) self.assertEqual(result[-1][-1], 11) def test_subsample_2d_avg(self): array = np.array([[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14], [15, 16, 17, 18, 19, 20, 21]]) result = samp.subsample_2d(array, 3, "average") self.assertEqual(result.shape, (3, 2)) self.assertEqual(result[0][0], 2) self.assertEqual(result[-1][-1], 19) def test_subsample_2d_med(self): array = np.array([[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14], [15, 16, 17, 18, 19, 20, 21]]) result = samp.subsample_2d(array, 3, "median") self.assertEqual(result.shape, (3, 2)) self.assertEqual(result[0][0], 2) self.assertEqual(result[-1][-1], 19) def test_subsample_2d_max(self): array = np.array([[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14], [15, 16, 17, 18, 19, 20, 21]]) result = samp.subsample_2d(array, 3, "max") self.assertEqual(result.shape, (3, 2)) self.assertEqual(result[0][0], 3) self.assertEqual(result[-1][-1], 20) def test_subsample_2d_min(self): array = np.array([[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14], [15, 16, 17, 18, 19, 20, 21]]) result = samp.subsample_2d(array, 3, "min") self.assertEqual(result.shape, (3, 2)) self.assertEqual(result[0][0], 1) self.assertEqual(result[-1][-1], 18) def test_decimate_timeseries(self): result = samp.decimate_timeseries(np.concatenate([self.timeseries, self.timeseries]), 3) self.assertEqual(len(result), 10) self.assertAlmostEqual(result[0], 9.80, 2) self.assertAlmostEqual(result[-1], 19.35, 2) def test_decimate_timeseries_2(self): result = samp.decimate_timeseries(np.concatenate([self.timeseries, self.timeseries]), 2) self.assertEqual(len(result), 14) self.assertAlmostEqual(result[0], 9.94, 2) self.assertAlmostEqual(result[-1], 29.28, 2) def test_decimate_collection(self): repeated_timeseries = np.concatenate([self.timeseries, self.timeseries]) result = samp.decimate_timeseries_collection(np.array([repeated_timeseries]), 3) self.assertEqual(len(result), 1) self.assertEqual(len(result[0]), 10) self.assertAlmostEqual(result[0][0], 9.80, 2) self.assertAlmostEqual(result[0][-1], 19.35, 2)Ancestors
- unittest.case.TestCase
Static methods
def setUpClass() ‑> None-
Hook method for setting up class fixture before running tests in the class.
Methods
def test_decimate_collection(self)def test_decimate_timeseries(self)def test_decimate_timeseries_2(self)def test_resample_uneven_timestamps(self)def test_resample_uneven_timestamps_assumed(self)def test_resample_with_sample_rate(self)def test_subsample(self)def test_subsample_2d(self)def test_subsample_2d_avg(self)def test_subsample_2d_max(self)def test_subsample_2d_med(self)def test_subsample_2d_min(self)def test_subsample_avg(self)def test_subsample_max(self)def test_subsample_med(self)def test_subsample_min(self)