diff --git a/selfdrive/locationd/test/test_locationd_scenarios.py b/selfdrive/locationd/test/test_locationd_scenarios.py index f08fe72ff1..d2455ef9e0 100755 --- a/selfdrive/locationd/test/test_locationd_scenarios.py +++ b/selfdrive/locationd/test/test_locationd_scenarios.py @@ -49,8 +49,7 @@ def get_select_fields_data(logs): return data -def run_scenarios(scenario): - logs = list(LogReader(get_url(TEST_ROUTE, TEST_SEG_NUM))) +def run_scenarios(scenario, logs): if scenario == Scenario.BASE: pass @@ -104,6 +103,11 @@ class TestLocationdScenarios(unittest.TestCase): - locationd kalman filter should never go unstable (we care mostly about yaw_rate, roll, gpsOK, inputsOK, sensorsOK) - faulty values should be ignored, with appropriate flags set """ + + @classmethod + def setUpClass(cls): + cls.logs = list(LogReader(get_url(TEST_ROUTE, TEST_SEG_NUM))) + def test_base(self): """ Test: unchanged log @@ -111,7 +115,7 @@ class TestLocationdScenarios(unittest.TestCase): - yaw_rate: unchanged - roll: unchanged """ - orig_data, replayed_data = run_scenarios(Scenario.BASE) + orig_data, replayed_data = run_scenarios(Scenario.BASE, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5))) @@ -123,7 +127,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: - gpsOK: False """ - orig_data, replayed_data = run_scenarios(Scenario.GPS_OFF) + orig_data, replayed_data = run_scenarios(Scenario.GPS_OFF, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5))) self.assertTrue(np.all(replayed_data['gps_flag'] == 0.0)) @@ -136,7 +140,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: - gpsOK: True for the first half, False for the second half """ - orig_data, replayed_data = run_scenarios(Scenario.GPS_OFF_MIDWAY) + orig_data, replayed_data = run_scenarios(Scenario.GPS_OFF_MIDWAY, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5))) self.assertTrue(np.diff(replayed_data['gps_flag'])[512] == -1.0) @@ -149,7 +153,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: - gpsOK: False for the first half, True for the second half """ - orig_data, replayed_data = run_scenarios(Scenario.GPS_ON_MIDWAY) + orig_data, replayed_data = run_scenarios(Scenario.GPS_ON_MIDWAY, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(1.5))) self.assertTrue(np.diff(replayed_data['gps_flag'])[505] == 1.0) @@ -162,7 +166,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: - gpsOK: False for the middle section, True for the rest """ - orig_data, replayed_data = run_scenarios(Scenario.GPS_TUNNEL) + orig_data, replayed_data = run_scenarios(Scenario.GPS_TUNNEL, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5))) self.assertTrue(np.diff(replayed_data['gps_flag'])[213] == -1.0) @@ -176,7 +180,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: 0 - sensorsOK: False """ - _, replayed_data = run_scenarios(Scenario.GYRO_OFF) + _, replayed_data = run_scenarios(Scenario.GYRO_OFF, self.logs) self.assertTrue(np.allclose(replayed_data['yaw_rate'], 0.0)) self.assertTrue(np.allclose(replayed_data['roll'], 0.0)) self.assertTrue(np.all(replayed_data['sensors_flag'] == 0.0)) @@ -189,7 +193,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: unchanged - inputsOK: False for some time after the spike, True for the rest """ - orig_data, replayed_data = run_scenarios(Scenario.GYRO_SPIKE_MIDWAY) + orig_data, replayed_data = run_scenarios(Scenario.GYRO_SPIKE_MIDWAY, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5))) self.assertTrue(np.diff(replayed_data['inputs_flag'])[500] == -1.0) @@ -203,7 +207,7 @@ class TestLocationdScenarios(unittest.TestCase): - roll: 0 - sensorsOK: False """ - _, replayed_data = run_scenarios(Scenario.ACCEL_OFF) + _, replayed_data = run_scenarios(Scenario.ACCEL_OFF, self.logs) self.assertTrue(np.allclose(replayed_data['yaw_rate'], 0.0)) self.assertTrue(np.allclose(replayed_data['roll'], 0.0)) self.assertTrue(np.all(replayed_data['sensors_flag'] == 0.0)) @@ -214,7 +218,7 @@ class TestLocationdScenarios(unittest.TestCase): Test: an accelerometer spike in the middle of the segment Expected Result: Right now, the kalman filter is not robust to small spikes like it is to gyroscope spikes. """ - orig_data, replayed_data = run_scenarios(Scenario.ACCEL_SPIKE_MIDWAY) + orig_data, replayed_data = run_scenarios(Scenario.ACCEL_SPIKE_MIDWAY, self.logs) self.assertTrue(np.allclose(orig_data['yaw_rate'], replayed_data['yaw_rate'], atol=np.radians(0.2))) self.assertTrue(np.allclose(orig_data['roll'], replayed_data['roll'], atol=np.radians(0.5)))