def test_subtract_bias_fails():
ccd_data = ccd_data_func(data_size=50)
# Should fail if shapes don't match
bias = CCDData(np.array([200, 200]), unit=u.adu)
with pytest.raises(ValueError):
subtract_bias(ccd_data, bias)
# Should fail because units don't match
bias = CCDData(np.zeros_like(ccd_data), unit=u.meter)
with pytest.raises(u.UnitsError):
subtract_bias(ccd_data, bias)
def test_subtract_bias_does_not_change_input():
ccd_data = ccd_data_func()
original = ccd_data.copy()
master_frame = CCDData(np.zeros_like(ccd_data), unit=ccd_data.unit)
ccd = subtract_bias(ccd_data, master=master_frame)
np.testing.assert_array_equal(original.data, ccd_data.data)
assert original.unit == ccd_data.unit
def test_subtract_bias():
ccd_data = ccd_data_func()
data_avg = ccd_data.data.mean()
bias_level = 5.0
ccd_data.data = ccd_data.data + bias_level
ccd_data.header['key'] = 'value'
master_bias_array = np.zeros_like(ccd_data.data) + bias_level
master_bias = CCDData(master_bias_array, unit=ccd_data.unit)
no_bias = subtract_bias(ccd_data, master_bias, add_keyword=None)
# Does the data we are left with have the correct average?
np.testing.assert_almost_equal(no_bias.data.mean(), data_avg)
# With logging turned off, metadata should not change
assert no_bias.header == ccd_data.header
del no_bias.header['key']
assert 'key' in ccd_data.header
assert no_bias.header is not ccd_data.header