def test_pattern_noise_on_2d_image(set_random_seed):
image = FakeImage()
image.data = generate_data(has_pattern_noise=True)
detector = pattern_noise.PatternNoiseDetector(None)
logger.error = mock.MagicMock()
detector.do_stage(image)
assert logger.error.called
def test_pattern_noise_does_not_detect_stars(set_random_seed):
data = generate_data()
for i in range(5):
x = np.random.uniform(low=0.0, high=100)
y = np.random.uniform(low=0.0, high=100)
brightness = np.random.uniform(low=1000., high=5000.)
data += gaussian2d(data.shape, x, y, brightness, 3.5)
detector = pattern_noise.PatternNoiseDetector(None)
assert detector.check_for_pattern_noise(data)[0] == False
def test_pattern_noise_on_3d_image(mock_save_qc):
data = 100.0 * np.sin(np.arange(1000000 * 4) / 0.1) + 1000.0 + np.random.normal(0.0, 10.0, size=1000000 * 4)
data = data.reshape(4, 1000, 1000)
image = FakeImage()
image.data = data
detector = pattern_noise.PatternNoiseDetector(None)
_ = detector.do_stage([image])
assert mock_save_qc.called
def test_pattern_noise_in_only_one_quadrant(mock_save_qc):
data = np.random.normal(0.0, 10.0, size=1000000 * 4) + 1000.0
data = data.reshape(4, 1000, 1000)
data[3] += 100.0 * np.sin(np.arange(1e6) / 0.1).reshape(1000, 1000)
image = FakeImage()
image.data = data
detector = pattern_noise.PatternNoiseDetector(None)
_ = detector.do_stage([image])
assert mock_save_qc.called
def test_group_by_keywords():
detector = pattern_noise.PatternNoiseDetector(None)
assert detector.group_by_keywords is None
def test_no_input_images():
detector = pattern_noise.PatternNoiseDetector(None)
images = detector.do_stage([])
assert len(images) == 0
def test_pattern_noise_does_not_detect_white_noise(set_random_seed):
data = generate_data()
detector = pattern_noise.PatternNoiseDetector(None)
assert detector.check_for_pattern_noise(data)[0] == False
def test_pattern_noise_detects_noise_when_it_should(set_random_seed):
data = generate_data(has_pattern_noise=True)
detector = pattern_noise.PatternNoiseDetector(None)
assert detector.check_for_pattern_noise(data)[0]
def test_null_input_image():
detector = pattern_noise.PatternNoiseDetector(None)
image = detector.run(None)
assert image is None