def test_header_master_bias_level_returns_2(mock_image):
maker = BiasMaker(FakeContext())
maker.do_stage([FakeBiasImage(image_multiplier=2.0) for x in range(6)])
args, kwargs = mock_image.call_args
header = kwargs['header']
assert header['BIASLVL'] == 2.0
def test_header_cal_type_bias(mock_image):
maker = BiasMaker(FakeContext())
maker.do_stage([FakeBiasImage() for x in range(6)])
args, kwargs = mock_image.call_args
header = kwargs['header']
assert header['OBSTYPE'].upper() == 'BIAS'
def test_header_cal_type_bias(mock_frame, mock_namer):
mock_namer.return_value = lambda *x: 'foo.fits'
nx = 101
ny = 103
context = make_context_with_master_bias(bias_level=0.0,
readnoise=10.0,
nx=nx,
ny=ny)
maker = BiasMaker(context)
images = maker.do_stage([FakeBiasImage(nx=nx, ny=ny) for x in range(6)])
assert images[0].header['OBSTYPE'].upper() == 'BIAS'
def test_makes_a_sensible_master_bias(mock_frame, mock_namer):
mock_namer.return_value = lambda *x: 'foo.fits'
nimages = 20
expected_readnoise = 15.0
images = [FakeBiasImage() for x in range(nimages)]
for image in images:
image.data = np.random.normal(loc=0.0,
scale=expected_readnoise,
size=(image.ny, image.nx))
maker = BiasMaker(FakeContext(frame_class=FakeBiasImage))
stacked_images = maker.do_stage(images)
master_bias = stacked_images[0].data
assert np.abs(np.mean(master_bias)) < 0.1
actual_readnoise = np.std(master_bias)
assert np.abs(actual_readnoise - expected_readnoise / (nimages**0.5)) < 0.2
def test_bias_level_is_average_of_inputs(mock_instrument_info, mock_namer):
mock_namer.return_value = lambda *x: 'foo.fits'
nimages = 20
bias_levels = np.arange(nimages, dtype=float)
images = [FakeBiasImage(bias_level=i) for i in bias_levels]
mock_instrument_info.return_value = None, None, None
fake_context = FakeContext()
fake_context.db_address = ''
maker = BiasMaker(fake_context)
master_bias = maker.do_stage(images)[0]
header = master_bias.header
assert header['BIASLVL'] == np.mean(bias_levels)
def test_makes_a_sensible_master_bias(mock_images):
nimages = 20
expected_bias = 1183.0
expected_readnoise = 15.0
images = [FakeBiasImage() for x in range(nimages)]
for image in images:
image.data = np.random.normal(loc=expected_bias, scale=expected_readnoise,
size=(image.ny, image.nx))
maker = BiasMaker(FakeContext())
maker.do_stage(images)
args, kwargs = mock_images.call_args
master_bias = kwargs['data']
assert np.abs(np.mean(master_bias)) < 0.1
actual_bias = float(kwargs['header']['BIASLVL'])
assert np.abs(actual_bias - expected_bias) < 0.1
actual_readnoise = np.std(master_bias)
assert np.abs(actual_readnoise - expected_readnoise / (nimages ** 0.5)) < 0.2
def test_min_images():
bias_maker = BiasMaker(FakeContext())
processed_images = bias_maker.do_stage([])
assert len(processed_images) == 0
def test_group_by_attributes():
maker = BiasMaker(FakeContext())
assert maker.group_by_attributes() == ['configuration_mode', 'ccdsum']
def test_group_by_keywords():
maker = BiasMaker(None)
assert maker.group_by_keywords == ['ccdsum']
def test_min_images():
bias_maker = BiasMaker(None)
processed_images = bias_maker.do_stage([])
assert len(processed_images) == 0