def test_to_ccddata_invalid():
data = Data(label='not-an-image')
data.add_component(Component(np.array([3.4, 2.3, -1.1, 0.3]), units='Jy'),
'x')
with pytest.raises(ValueError) as exc:
data.get_object(CCDData, attribute=data.id['x'])
assert exc.value.args[
0] == 'Only 2-dimensional datasets can be converted to CCDData'
class FakeCoordinates(Coordinates):
def pixel_to_world_values(self, *pixel):
raise NotImplementedError()
def world_to_pixel_values(self, *pixel):
raise NotImplementedError()
coords = FakeCoordinates(n_dim=2)
coords.low_level_wcs = coords
data = Data(label='image-with-custom-coords', coords=coords)
data.add_component(Component(np.array([[3, 4], [4, 5]]), units='Jy'), 'x')
with pytest.raises(TypeError) as exc:
data.get_object(CCDData, attribute=data.id['x'])
assert exc.value.args[
0] == 'data.coords should be an instance of Coordinates or WCS'
def test_to_spectral_cube_missing_wcs():
data = Data(label='not-a-spectral-cube')
values = np.random.random((4, 5, 3))
data.add_component(Component(values, units='Jy'), 'x')
with pytest.raises(TypeError) as exc:
data.get_object(SpectralCube, attribute=data.id['x'])
assert exc.value.args[0] == ('data.coords should be an instance of BaseLowLevelWCS.')
def test_to_spectral_cube_invalid_ndim():
data = Data(label='not-a-spectral-cube')
data.add_component(Component(np.array([3.4, 2.3, -1.1, 0.3]), units='Jy'), 'x')
with pytest.raises(ValueError) as exc:
data.get_object(SpectralCube, attribute=data.id['x'])
assert exc.value.args[0] == ('Data object should have 3 or 4 dimensions in order '
'to be converted to a SpectralCube object.')
def test_to_spectrum1d_invalid():
data = Data(label='not-a-spectrum')
data.add_component(Component(np.array([3.4, 2.3, -1.1, 0.3]), units='Jy'), 'x')
with pytest.raises(TypeError) as exc:
data.get_object(Spectrum1D, attribute=data.id['x'])
assert exc.value.args[0] == ('data.coords should be an instance of WCS '
'or SpectralCoordinates')
def test_to_ccddata(with_wcs):
if with_wcs:
coords = WCS_CELESTIAL
else:
coords = None
data = Data(label='image', coords=coords)
data.add_component(
Component(np.array([[3.4, 2.3], [-1.1, 0.3]]), units='Jy'), 'x')
image = data.get_object(CCDData, attribute=data.id['x'])
assert image.wcs is (WCS_CELESTIAL if with_wcs else None)
assert_allclose(image.data, [[3.4, 2.3], [-1.1, 0.3]])
assert image.unit is u.Jy
data.add_subset(data.id['x'] > 1, label='bright')
image_subset = data.get_subset_object(cls=CCDData,
subset_id=0,
attribute=data.id['x'])
assert image_subset.wcs is (WCS_CELESTIAL if with_wcs else None)
assert_allclose(image_subset.data, [[3.4, 2.3], [-1.1, 0.3]])
assert image_subset.unit is u.Jy
assert_equal(image_subset.mask, [[0, 0], [1, 1]])
def test_to_spectrum1d():
# Set up simple spectral WCS
wcs = WCS(naxis=1)
wcs.wcs.ctype = ['VELO-LSR']
wcs.wcs.set()
coords = WCSCoordinates(wcs=wcs)
data = Data(label='spectrum', coords=coords)
data.add_component(Component(np.array([3.4, 2.3, -1.1, 0.3]), units='Jy'),
'x')
spec = data.get_object(Spectrum1D, attribute=data.id['x'])
assert_quantity_allclose(spec.spectral_axis, [1, 2, 3, 4] * u.m / u.s)
assert_quantity_allclose(spec.flux, [3.4, 2.3, -1.1, 0.3] * u.Jy)
data.add_subset(data.id['x'] > 1, label='bright')
spec_subset = data.get_subset_object(cls=Spectrum1D,
subset_id=0,
attribute=data.id['x'])
assert_quantity_allclose(spec_subset.spectral_axis,
[1, 2, 3, 4] * u.m / u.s)
assert_quantity_allclose(spec_subset.flux,
[3.4, 2.3, np.nan, np.nan] * u.Jy)
assert_equal(spec_subset.mask, [1, 1, 0, 0])
def test_to_ccddata_unitless():
data = Data(label='image', coords=WCS_CELESTIAL)
data.add_component(Component(np.array([[3.4, 2.3], [-1.1, 0.3]])), 'x')
image = data.get_object(CCDData, attribute=data.id['x'])
assert_allclose(image.data, [[3.4, 2.3], [-1.1, 0.3]])
assert image.unit is u.one
def test_to_spectral_cube_unitless(spectral_cube_wcs):
data = Data(label='spectral_cube', coords=spectral_cube_wcs)
values = np.random.random((4, 5, 3))
data.add_component(Component(values), 'x')
spec = data.get_object(SpectralCube, attribute=data.id['x'])
assert_quantity_allclose(spec.spectral_axis, [1, 2, 3, 4] * u.m / u.s)
assert_quantity_allclose(spec.filled_data[...], values * u.one)
def test_translator_from_data():
# Case where the initial data object wasn't originally created from a
# DataFrame.
data = Data()
data['a'] = [3, 5, 6, 7]
data['b'] = [1.5, 2.2, 1.3, 3.3]
data['c'] = ['r', 'd', 'w', 'q']
with pytest.raises(ValueError) as exc:
df = data.get_object()
assert exc.value.args[0] == ('Specify the object class to use with cls= - supported '
'classes are:\n\n* pandas.core.frame.DataFrame')
df = data.get_object(cls=DataFrame)
assert_equal(list(df.columns), ['a', 'b', 'c'])
assert_equal(df['a'].values, [3, 5, 6, 7])
assert_equal(df['b'].values, [1.5, 2.2, 1.3, 3.3])
assert_equal(df['c'].values, ['r', 'd', 'w', 'q'])
def test_to_spectral_cube_invalid_wcs():
wcs = WCS(naxis=3)
data = Data(label='not-a-spectral-cube', coords=wcs)
values = np.random.random((4, 5, 3))
data.add_component(Component(values, units='Jy'), 'x')
with pytest.raises(ValueError) as exc:
data.get_object(SpectralCube, attribute=data.id['x'])
assert exc.value.args[0] == ('No celestial axes found in WCS')
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', '']
data = Data(label='not-a-spectral-cube', coords=wcs)
values = np.random.random((4, 5, 3))
data.add_component(Component(values, units='Jy'), 'x')
with pytest.raises(ValueError) as exc:
data.get_object(SpectralCube, attribute=data.id['x'])
assert exc.value.args[0] == ('No spectral axes found in WCS')
def test_to_spectrum1d_with_spectral_coordinates():
coords = SpectralCoordinates([1, 4, 10] * u.micron)
data = Data(label='spectrum1d', coords=coords)
data.add_component(Component(np.array([3, 4, 5]), units='Jy'), 'x')
assert_allclose(data.coords.pixel2world([0, 0.5, 1, 1.5, 2]),
[[1, 2.5, 4, 7, 10]])
spec = data.get_object(Spectrum1D, attribute=data.id['x'])
assert_quantity_allclose(spec.spectral_axis, [1, 4, 10] * u.micron)
assert_quantity_allclose(spec.flux, [3, 4, 5] * u.Jy)
def test_to_ccddata_default_attribute():
data = Data(label='image', coords=WCS_CELESTIAL)
with pytest.raises(ValueError) as exc:
data.get_object(CCDData)
assert exc.value.args[0] == 'Data object has no attributes.'
data.add_component(Component(np.array([[3, 4], [5, 6]]), units='Jy'), 'x')
image = data.get_object(CCDData)
assert_allclose(image.data, [[3, 4], [5, 6]])
assert image.unit is u.Jy
data.add_component(Component(np.array([[3, 4], [5, 6]]), units='Jy'), 'y')
with pytest.raises(ValueError) as exc:
data.get_object(CCDData)
assert exc.value.args[0] == ('Data object has more than one attribute, so '
'you will need to specify which one to use as '
'the flux for the spectrum using the attribute= '
'keyword argument.')
def test_to_spectral_cube_default_attribute(spectral_cube_wcs):
data = Data(label='spectral_cube', coords=spectral_cube_wcs)
values = np.random.random((4, 5, 3))
with pytest.raises(ValueError) as exc:
data.get_object(SpectralCube)
assert exc.value.args[0] == 'Data object has no attributes.'
data.add_component(Component(values, units='Jy'), 'x')
spec = data.get_object(SpectralCube)
assert_quantity_allclose(spec.filled_data[...], values * u.Jy)
data.add_component(Component(values, units='Jy'), 'y')
with pytest.raises(ValueError) as exc:
data.get_object(SpectralCube)
assert exc.value.args[0] == ('Data object has more than one attribute, so '
'you will need to specify which one to use as '
'the flux for the spectral cube using the attribute= '
'keyword argument.')
def test_to_spectrum1d_from_3d_cube():
# Set up simple spectral WCS
wcs = WCS(naxis=3)
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'VELO-LSR']
wcs.wcs.set()
data = Data(label='spectral-cube', coords=wcs)
data.add_component(Component(np.ones((3, 4, 5)), units='Jy'), 'x')
spec = data.get_object(Spectrum1D, attribute=data.id['x'], statistic='sum')
assert_quantity_allclose(spec.spectral_axis, [1, 2, 3] * u.m / u.s)
assert_quantity_allclose(spec.flux, [20, 20, 20] * u.Jy)
def test_to_spectrum1d_unitless():
# Set up simple spectral WCS
wcs = WCS(naxis=1)
wcs.wcs.ctype = ['VELO-LSR']
wcs.wcs.set()
data = Data(label='spectrum', coords=wcs)
data.add_component(Component(np.array([3.4, 2.3, -1.1, 0.3])), 'x')
spec = data.get_object(Spectrum1D, attribute=data.id['x'])
assert_quantity_allclose(spec.spectral_axis, [1, 2, 3, 4] * u.m / u.s)
assert_quantity_allclose(spec.flux, [3.4, 2.3, -1.1, 0.3] * u.one)
def test_to_spectrum1d_default_attribute():
coords = SpectralCoordinates([1, 4, 10] * u.micron)
data = Data(label='spectrum1d', coords=coords)
with pytest.raises(ValueError) as exc:
data.get_object(Spectrum1D)
assert exc.value.args[0] == 'Data object has no attributes.'
data.add_component(Component(np.array([3, 4, 5]), units='Jy'), 'x')
spec = data.get_object(Spectrum1D)
assert_quantity_allclose(spec.flux, [3, 4, 5] * u.Jy)
data.add_component(Component(np.array([3, 4, 5]), units='Jy'), 'y')
with pytest.raises(ValueError) as exc:
data.get_object(Spectrum1D)
assert exc.value.args[0] == ('Data object has more than one attribute, so '
'you will need to specify which one to use as '
'the flux for the spectrum using the attribute= '
'keyword argument.')
def test_translator_from_data_with_derived():
# Case where we convert a subset to a DataFrame
data = Data()
data['a'] = [3, 5, 6, 7]
data['b'] = data.id['a'] + 1
dc = DataCollection([data])
dc.new_subset_group(label='test subset', subset_state=data.id['b'] > 2)
df = data.get_object(cls=DataFrame)
assert_equal(list(df.columns), ['a', 'b'])
assert_equal(df['a'].values, [3, 5, 6, 7])
assert_equal(df['b'].values, [4, 6, 7, 8])
def test_to_spectral_cube(spectral_cube_wcs):
data = Data(label='spectral_cube', coords=spectral_cube_wcs)
values = np.random.random((4, 5, 3))
data.add_component(Component(values, units='Jy'), 'x')
spec = data.get_object(SpectralCube, attribute=data.id['x'])
assert_quantity_allclose(spec.spectral_axis, [1, 2, 3, 4] * u.m / u.s)
assert_quantity_allclose(spec.filled_data[...], values * u.Jy)
data.add_subset(data.id['x'] > 0.5, label='bright')
spec_subset = data.get_subset_object(cls=SpectralCube, subset_id=0,
attribute=data.id['x'])
assert_quantity_allclose(spec_subset.spectral_axis, [1, 2, 3, 4] * u.m / u.s)
expected = values.copy()
expected[expected <= 0.5] = np.nan
assert_quantity_allclose(spec_subset.filled_data[...], expected * u.Jy)
assert_equal(spec_subset.mask.include(), values > 0.5)
