def test_make_residual():
"""Test make_residual"""
fitsfile = 'tests/test_files/1904-66_SIN.fits'
catalog = 'tests/test_files/1904_comp.fits'
residual = 'tests/temp/residual.fits'
masked = 'tests/temp/masked.fits'
# default operation
ar.make_residual(fitsfile=fitsfile,
catalog=catalog,
rfile=residual,
mfile=masked,
mask=False)
if not os.path.exists(masked):
raise AssertionError("Mask file not written")
os.remove(masked)
# with masking
ar.make_residual(fitsfile=fitsfile,
catalog=catalog,
rfile=residual,
mfile=masked,
mask=True)
if not os.path.exists(masked):
raise AssertionError("Mask file not written")
os.remove(masked)
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():
"""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_residual():
"""Test make_residual"""
fitsfile = 'tests/test_files/1904-66_SIN.fits'
catalog = 'tests/test_files/1904_comp.fits'
residual = 'tests/temp/residual.fits'
masked = 'tests/temp/masked.fits'
# default operation
ar.make_residual(fitsfile=fitsfile, catalog=catalog,
rfile=residual, mfile=masked, mask=False)
if not os.path.exists(masked):
raise AssertionError("Mask file not written")
os.remove(masked)
# with masking
ar.make_residual(fitsfile=fitsfile, catalog=catalog,
rfile=residual, mfile=masked, mask=True)
if not os.path.exists(masked):
raise AssertionError("Mask file not written")
os.remove(masked)
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")
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
src.island = 102
src.peak_flux = 80 * noise
cat.append(src)
return cat
if __name__ == "__main__":
# configure logging
logging.basicConfig(format="%(module)s:%(levelname)s %(message)s")
log = logging.getLogger("Aegean")
logging_level = logging.DEBUG # if options.debug else logging.INFO
log.setLevel(logging_level)
image, wcs = make_noise_image()
header = wcs.to_header()
header['BMAJ'] = psf[0] / 3600
header['BMIN'] = psf[1] / 3600
header['BPA'] = psf[2]
hdu = fits.HDUList(hdus=[fits.PrimaryHDU(header=header, data=image)])
header = hdu[0].header
cat = make_catalogue()
wcshelper = WCSHelper.from_header(header)
hdu[0].data += AeRes.make_model(cat, image.shape, wcshelper)
hdu.writeto('synthetic_test.fits', overwrite=True)
psf_hdu = make_psf_map(hdu)
psf_hdu.writeto('synthetic_test_psf.fits', overwrite=True)