def test_header_has_biaslevel(mock_cal, mock_frame):
nx = 101
ny = 103
context = make_context_with_master_bias(nx=nx, ny=ny)
subtractor = BiasSubtractor(context)
image = subtractor.do_stage(FakeImage(nx=nx, ny=ny))
assert image.header.get('BIASLVL') == 0.0
def test_raises_exception_if_no_master_calibration(mock_cal, mock_images):
mock_cal.return_value = None
mock_images.return_value = FakeBiasImage()
subtractor = BiasSubtractor(None)
with pytest.raises(MasterCalibrationDoesNotExist):
images = subtractor.do_stage([FakeImage() for x in range(6)])
def test_raises_exception_if_no_master_calibration(mock_cal, mock_images):
mock_cal.return_value = None
mock_images.return_value = FakeBiasImage()
subtractor = BiasSubtractor(None)
with pytest.raises(MasterCalibrationDoesNotExist):
images = subtractor.do_stage([FakeImage() for x in range(6)])
def test_flags_image_if_no_master_calibration(mock_cal):
mock_cal.return_value = None
nx = 101
ny = 103
context = make_context_with_master_bias(nx=nx, ny=ny)
subtractor = BiasSubtractor(context)
image = subtractor.do_stage(FakeImage(nx=nx, ny=ny) )
assert image.is_bad is True
def test_header_biaslevel_is_2(mock_cal, mock_frame):
nx = 101
ny = 103
mock_frame.return_value = FakeBiasImage(bias_level=2.0, nx=nx, ny=ny)
context = make_context_with_master_bias(bias_level=2.0, readnoise=10.0, nx=nx, ny=ny)
subtractor = BiasSubtractor(context)
image = subtractor.do_stage(FakeImage(nx=nx, ny=ny))
assert image.header.get('BIASLVL') == 2.0
def test_bias_subtraction_is_reasonable(mock_frame, mock_cal):
mock_cal.return_value = 'test.fits'
input_bias = 1000.0
input_readnoise = 9.0
input_level = 2000.0
nx = 101
ny = 103
mock_frame.return_value = FakeBiasImage(bias_level=input_bias, data=np.random.normal(0.0, input_readnoise, size=(ny, nx)),
nx=nx, ny=ny)
# TODO: make_context_with_master_bias can now go away. Just use a FakeContext instead
context = make_context_with_master_bias(bias_level=input_bias, readnoise=input_readnoise, nx=nx, ny=ny)
subtractor = BiasSubtractor(context)
image = FakeImage(image_multiplier=input_level)
image = subtractor.do_stage(image)
assert np.abs(image.header.get('BIASLVL') - input_bias) < 1.0
assert np.abs(np.mean(image.data) - (input_level - input_bias)) < 1.0
def test_bias_subtraction_is_reasonable(mock_cal, mock_image):
mock_cal.return_value = 'test.fits'
input_bias = 1000.0
input_readnoise = 9.0
input_level = 2000.0
nx = 101
ny = 103
fake_master_bias = FakeBiasImage(bias_level=input_bias)
fake_master_bias.data = np.random.normal(0.0, input_readnoise, size=(ny, nx))
mock_image.return_value = fake_master_bias
subtractor = BiasSubtractor(None)
images = [FakeImage(image_multiplier=input_level) for x in range(6)]
images = subtractor.do_stage(images)
for image in images:
assert np.abs(image.header['BIASLVL'][0] - input_bias) < 1.0
assert np.abs(np.mean(image.data) - input_level + input_bias) < 1.0
def test_bias_subtraction_is_reasonable(mock_cal, mock_image):
mock_cal.return_value = 'test.fits'
input_bias = 1000.0
input_readnoise = 9.0
input_level = 2000.0
nx = 101
ny = 103
fake_master_bias = FakeBiasImage(bias_level=input_bias)
fake_master_bias.data = np.random.normal(0.0,
input_readnoise,
size=(ny, nx))
mock_image.return_value = fake_master_bias
subtractor = BiasSubtractor(None)
images = [FakeImage(image_multiplier=input_level) for x in range(6)]
images = subtractor.do_stage(images)
for image in images:
assert np.abs(image.header['BIASLVL'][0] - input_bias) < 1.0
assert np.abs(np.mean(image.data) - input_level + input_bias) < 1.0
def test_null_input_image():
subtractor = BiasSubtractor(FakeContext())
image = subtractor.run(None)
assert image is None
def test_header_biaslevel_is_2(mock_cal, mock_image):
mock_image.return_value = FakeBiasImage(bias_level=2.0)
subtractor = BiasSubtractor(None)
images = subtractor.do_stage([FakeImage() for x in range(6)])
for image in images:
assert image.header['BIASLVL'][0] == 2
def test_no_input_images():
subtractor = BiasSubtractor(None)
images = subtractor.do_stage([])
assert len(images) == 0
def test_header_biaslevel_is_2(mock_cal, mock_image):
mock_image.return_value = FakeBiasImage(bias_level=2.0)
subtractor = BiasSubtractor(None)
images = subtractor.do_stage([FakeImage() for x in range(6)])
for image in images:
assert image.header['BIASLVL'][0] == 2
def test_group_by_keywords():
subtractor = BiasSubtractor(None)
assert subtractor.group_by_keywords == ['ccdsum']
def test_no_input_images():
subtractor = BiasSubtractor(None)
images = subtractor.do_stage([])
assert len(images) == 0