def test_header_cal_type_flat(mock_instrument_info, mock_namer):
mock_namer.return_value = lambda *x: 'foo.fits'
mock_instrument_info.return_value = None, None, None
fake_context = FakeContext()
fake_context.db_address = ''
maker = FlatMaker(fake_context)
master_flat = maker.do_stage([FakeFlatImage() for x in range(6)])[0]
header = master_flat.header
assert header['OBSTYPE'].upper() == 'SKYFLAT'
def test_bias_level_is_average_of_inputs(mock_instrument_info):
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_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)