def test_stack_calibrations(self, mock_get_instrument,
mock_get_calibration_images,
mock_process_master_maker, setup):
mock_get_instrument.return_value = self.fake_inst
mock_get_calibration_images.return_value = [
FakeBiasImage(), FakeBiasImage()
]
stack_calibrations(self.min_date, self.max_date, 1, self.frame_type,
self.context,
[self.fake_blocks_response_json['results'][0]])
mock_process_master_maker.assert_called_with(self.fake_inst,
self.frame_type,
self.min_date,
self.max_date, ANY)
def test_stack_calibrations_not_enough_images(self, mock_get_instrument,
mock_get_calibration_images,
mock_process_master_maker,
setup):
mock_get_instrument.return_value = self.fake_inst
mock_get_calibration_images.return_value = [FakeBiasImage()]
with pytest.raises(Retry) as e:
stack_calibrations(self.min_date, self.max_date, 1,
self.frame_type, self.context,
[self.fake_blocks_response_json['results'][0]])
assert e.type is Retry
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)