def test_load_sources():
"""Test load_sources"""
filename = 'tests/test_files/1904_comp.fits'
cat = ar.load_sources(filename)
if cat is None:
raise AssertionError("load_sources failed")
return
def test_make_masked_model():
"""Test make_model when a mask is being used"""
filename = 'tests/test_files/1904_comp.fits'
sources = ar.load_sources(filename)
hdulist = fits.open('tests/test_files/1904-66_SIN.fits')
wcs_helper = wcs_helpers.WCSHelper.from_header(header=hdulist[0].header)
# test mask with sigma
model = ar.make_model(sources=sources,
shape=hdulist[0].data.shape,
wcshelper=wcs_helper,
mask=True)
finite = np.where(np.isfinite(model))
if np.all(model == 0.):
raise AssertionError("Model is empty")
if not np.any(np.isnan(model)):
raise AssertionError("Model is not masked")
if not np.all(model[finite] == 0.):
raise AssertionError("Model has values that are not zero or nan")
# test mask with frac
model = ar.make_model(sources=sources,
shape=hdulist[0].data.shape,
wcshelper=wcs_helper,
mask=True,
frac=0.1)
finite = np.where(np.isfinite(model))
if np.all(model == 0.):
raise AssertionError("Model is empty")
if not np.any(np.isnan(model)):
raise AssertionError("Model is not masked")
if not np.all(model[finite] == 0.):
raise AssertionError("Model has values that are not zero or nan")
def test_make_model():
"""Test make_modell"""
filename = 'tests/test_files/1904_comp.fits'
sources = ar.load_sources(filename)
hdulist = fits.open('tests/test_files/1904-66_SIN.fits')
wcs_helper = wcs_helpers.WCSHelper.from_header(header=hdulist[0].header)
# regular run
model = ar.make_model(sources=sources, shape=hdulist[0].data.shape,
wcshelper=wcs_helper)
if np.all(model == 0.):
raise AssertionError("Model is empty")
# model with *all* sources outside region
# shape (100,2) means we only sometimes reject a source based on it's x-coord
model = ar.make_model(sources=sources, shape=(100, 2),
wcshelper=wcs_helper)
if not np.all(model == 0.):
raise AssertionError("Model is *not* empty")
# test mask with sigma
model = ar.make_model(sources=sources, shape=hdulist[0].data.shape,
wcshelper=wcs_helper, mask=True)
if np.all(model == 0.):
raise AssertionError("Model is empty")
if not np.any(np.isnan(model)):
raise AssertionError("Model is not masked")
# test mask with frac
model = ar.make_model(sources=sources, shape=hdulist[0].data.shape,
wcshelper=wcs_helper, mask=True, frac=0.1)
if np.all(model == 0.):
raise AssertionError("Model is empty")
if not np.any(np.isnan(model)):
raise AssertionError("Model is not masked")
def test_load_sources():
"""Test load_sources"""
filename = 'tests/test_files/1904_comp.fits'
cat = ar.load_sources(filename)
if cat is None:
raise AssertionError("load_sources_failed")
return
def test_load_sources_missing_columns():
filename = 'tests/test_files/1904_comp.fits'
table = catalogs.load_table(filename)
table.rename_column('ra', 'RAJ2000')
table.write('dlme.fits')
cat = ar.load_sources('dlme.fits')
if os.path.exists('dlme.fits'):
os.remove('dlme.fits')
if cat is not None:
raise AssertionError("Missing columns should be caught, but weren't")
return
def test_load_soruces_renamed_columns():
"""Test load_sources with renamed columns"""
filename = 'tests/test_files/1904_comp_renamed_cols.fits'
colnames = {
'ra_col': 'RAJ2000',
'dec_col': 'DEJ2000',
'peak_col': 'S',
'a_col': 'bmaj',
'b_col': 'bmin',
'pa_col': 'bpa'
}
cat = ar.load_sources(filename, **colnames)
if cat is None:
raise AssertionError("load_sources failed with renamed columns")
return
def test_make_model():
"""Test make_model"""
filename = 'tests/test_files/1904_comp.fits'
sources = ar.load_sources(filename)
hdulist = fits.open('tests/test_files/1904-66_SIN.fits')
wcs_helper = wcs_helpers.WCSHelper.from_header(header=hdulist[0].header)
# regular run
model = ar.make_model(sources=sources,
shape=hdulist[0].data.shape,
wcshelper=wcs_helper)
if np.all(model == 0.):
raise AssertionError("Model is empty")
# model with *all* sources outside region
# shape (100,2) means we only sometimes reject a source based on it's x-coord
model = ar.make_model(sources=sources,
shape=(100, 2),
wcshelper=wcs_helper)
if not np.all(model == 0.):
raise AssertionError("Model is *not* empty")