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 load_stacked_sequence(self, raster_data):
for window, window_data in raster_data.items():
w_data = Data(label=f"{window.replace(' ', '_')}")
w_data.coords = window_data.wcs
w_data.add_component(Component(window_data.data),
f"{window.replace(' ', '_')}")
w_data.style = VisualAttributes(color='#7A617C')
self.datasets.append(w_data)
def ds9_region(filename):
reg = regions.read_ds9(filename)
comp = Component(np.ones(len(reg), dtype='bool'))
data = RegionData(label='Regions: {0}'.format(os.path.split(filename)[-1]),
regions=reg)
return data
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_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_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_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_ticks_go_back_after_changing(self):
""" If you change to a categorical axis and then change back
to a numeric, the axis ticks should fix themselves properly.
"""
data = Data()
data.add_component(Component(np.arange(100)), 'y')
data.add_component(CategoricalComponent(['a'] * 50 + ['b'] * 50),
'xcat')
data.add_component(Component(2 * np.arange(100)), 'xcont')
self.add_data(data=data)
self.client.yatt = data.find_component_id('y')
self.client.xatt = data.find_component_id('xcat')
self.check_ticks(self.client.axes.xaxis, False, True)
self.check_ticks(self.client.axes.yaxis, False, False)
self.client.xatt = data.find_component_id('xcont')
self.check_ticks(self.client.axes.yaxis, False, False)
self.check_ticks(self.client.axes.xaxis, False, False)
def make_test_data():
data = Data(label="Test Cat Data 1")
np.random.seed(12345)
for letter in 'abcdefxyz':
comp = Component(np.random.random(100))
data.add_component(comp, letter)
return data
def make_test_data():
data = Data(label="Test Cube Data")
np.random.seed(12345)
for letter in 'abc':
comp = Component(np.random.random((10, 10, 10)))
data.add_component(comp, letter)
return data
def load_sunpy_map(self, sunpy_map):
sunpy_map_loaded = sunpy.map.Map(sunpy_map)
label = 'sunpy-map-' + sunpy_map_loaded.name
data = Data(label=label)
data.coords = sunpy_map_loaded.wcs # preferred way, preserves more info in some cases
data.meta = sunpy_map_loaded.meta
data.add_component(Component(sunpy_map_loaded.data),
sunpy_map_loaded.name)
data.style = VisualAttributes(color='#FDB813',
preferred_cmap=sunpy_map.cmap)
self.datasets.append(data)
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 panda_process(indf):
"""
Build a data set from a table using pandas. This attempts to respect
categorical data input by letting pandas.read_csv infer the type
"""
result = Data()
for name, column in indf.iteritems():
if (column.dtype == np.object) | (column.dtype == np.bool):
# try to salvage numerical data
try:
coerced = pd.to_numeric(column, errors='coerce')
except AttributeError: # pandas < 0.19
coerced = column.convert_objects(convert_numeric=True)
if (coerced.dtype !=
column.dtype) and coerced.isnull().mean() < 0.4:
c = Component(coerced.values)
else:
# pandas has a 'special' nan implementation and this doesn't
# play well with np.unique
c = CategoricalComponent(column.fillna(''))
else:
c = Component(column.values)
# convert header to string - in some cases if the first row contains
# numbers, these are cast to numerical types, so we want to change that
# here.
if not isinstance(name, six.string_types):
name = str(name)
# strip off leading #
name = name.strip()
if name.startswith('#'):
name = name[1:].strip()
result.add_component(c, name)
return result
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_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_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 setup_method(self, method):
"""
Parameters
----------
method
"""
# Set up simple spectral WCS
wcs_1d = WCS(naxis=1)
wcs_1d.wcs.ctype = ['VELO-LSR']
wcs_1d.wcs.set()
# Set up a spectral cube WCS
wcs_3d = WCS(naxis=3)
wcs_3d.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'VELO-LSR']
wcs_3d.wcs.set()
# Set up glue Coordinates object
coords_1d = WCSCoordinates(wcs=wcs_1d)
coords_3d = WCSCoordinates(wcs=wcs_3d)
self.data_1d = Data(label='spectrum', coords=coords_1d)
self.data_3d = Data(label='spectrum', coords=coords_3d)
# FIXME: there should be an easier way to do this in glue
x = np.array([3.4, 2.3, -1.1, 0.3])
y = np.array([3.2, 3.3, 3.4, 3.5])
self.data_1d.add_component(Component(x, units='Jy'), 'x')
self.data_1d.add_component(Component(y, units='Jy'), 'y')
self.data_3d.add_component(Component(np.broadcast_to(x, (6, 5, 4)), units='Jy'), 'x')
self.data_3d.add_component(Component(np.broadcast_to(x, (6, 5, 4))), 'y')
self.app = GlueApplication()
self.session = self.app.session
self.hub = self.session.hub
self.data_collection = self.session.data_collection
self.data_collection.append(self.data_1d)
self.data_collection.append(self.data_3d)
def load_sequence(self, raster_data):
for window, window_data in raster_data.items():
for i, scan_data in enumerate(window_data):
w_data = Data(
label=
f"{window.replace(' ', '_')}-{scan_data.meta['OBSID']}-scan-{i}"
)
w_data.coords = scan_data.wcs
w_data.add_component(Component(scan_data.data),
f"{window.replace(' ', '_')}-scan-{i}")
w_data.meta = scan_data.meta
w_data.style = VisualAttributes(color='#5A4FCF')
self.datasets.append(w_data)
def load_sji(self, sji):
with fits.open(sji) as hdul:
hdul.verify("fix")
label = hdul[0].header['TDESC1'] + hdul[0].header['OBSID']
data = Data(label=label)
data.coords = WCSCoordinates(hdul[0].header)
data.meta = hdul[0].header
preferred_cmap_name = 'IRIS ' + hdul[0].header['TDESC1'].replace(
'_', ' ')
data.style = VisualAttributes(preferred_cmap=preferred_cmap_name)
data.add_component(Component(hdul[0].data), label)
self.datasets.append(data)
def test_high_cardinatility_timing(self):
card = 50000
data = Data()
card_data = [str(num) for num in range(card)]
data.add_component(Component(np.arange(card * 5)), 'y')
data.add_component(CategoricalComponent(np.repeat([card_data], 5)),
'xcat')
self.add_data(data)
comp = data.find_component_id('xcat')
timer_func = partial(self.client._set_xydata, 'x', comp)
timer = timeit(timer_func, number=1)
assert timer < 3 # this is set for Travis speed
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_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 _parse_iris_raster(data, label):
"""
Parse IRIS Level 2 raster files so that it can be loaded by glue.
"""
w_dataset = []
for window, window_data in data.items():
for i, scan_data in enumerate(window_data):
w_data = Data(label=f"{window.replace(' ', '_')}-{scan_data.meta['OBSID']}-scan-{i}")
w_data.coords = WCSCoordinates(scan_data.wcs.to_header())
w_data.add_component(Component(scan_data.data), f"{window.replace(' ', '_')}-{scan_data.meta['OBSID']}-scan-{i}")
w_data.meta = scan_data.meta
w_data.style = VisualAttributes(color='#5A4FCF')
w_dataset.append(w_data)
return w_dataset
def test_conversion_utils_spectral_coordinates():
# Set up glue Coordinates object
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]])
assert glue_data_has_spectral_axis(data)
spec = glue_data_to_spectrum1d(data, 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_conversion_utils_3d():
# Set up simple spectral WCS
wcs = WCS(naxis=3)
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN', 'VELO-LSR']
wcs.wcs.set()
# Set up glue Coordinates object
coords = WCSCoordinates(wcs=wcs)
data = Data(label='spectral-cube', coords=coords)
data.add_component(Component(np.ones((3, 4, 5)), units='Jy'), 'x')
assert glue_data_has_spectral_axis(data)
spec = glue_data_to_spectrum1d(data, 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_conversion_utils_1d():
# Set up simple spectral WCS
wcs = WCS(naxis=1)
wcs.wcs.ctype = ['VELO-LSR']
wcs.wcs.set()
# Set up glue Coordinates object
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')
assert glue_data_has_spectral_axis(data)
spec = glue_data_to_spectrum1d(data, 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)
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)
def test_to_spectrum1d():
# 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]), 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, -1.1, 0.3] * u.Jy)
assert_equal(spec_subset.mask, [0, 0, 1, 1])