def test_print_special_operator_CompoundModel(capsys):
"""
Test that issue #11310 has been fixed
"""
model = convolve_models(models.Sersic2D(), models.Gaussian2D())
print(model)
true_out = "Model: CompoundModel\n" +\
"Inputs: ('x', 'y')\n" +\
"Outputs: ('z',)\n" +\
"Model set size: 1\n" +\
"Expression: convolve_fft (([0]), ([1]))\n" +\
"Components: \n" +\
" [0]: <Sersic2D(amplitude=1., r_eff=1., n=4., x_0=0., y_0=0., ellip=0., theta=0.)>\n" +\
"\n" +\
" [1]: <Gaussian2D(amplitude=1., x_mean=0., y_mean=0., x_stddev=1., y_stddev=1., theta=0.)>\n" +\
"Parameters:\n" +\
" amplitude_0 r_eff_0 n_0 x_0_0 y_0_0 ... y_mean_1 x_stddev_1 y_stddev_1 theta_1\n" +\
" ----------- ------- --- ----- ----- ... -------- ---------- ---------- -------\n" +\
" 1.0 1.0 4.0 0.0 0.0 ... 0.0 1.0 1.0 0.0\n"
out, err = capsys.readouterr()
assert err == ''
assert out == true_out
def test_print_special_operator_CompoundModel(capsys):
"""
Test that issue #11310 has been fixed
"""
model = convolve_models(models.Sersic2D(), models.Gaussian2D())
with astropy.conf.set_temp('max_width', 80):
assert str(model) == "Model: CompoundModel\n" +\
"Inputs: ('x', 'y')\n" +\
"Outputs: ('z',)\n" +\
"Model set size: 1\n" +\
"Expression: convolve_fft (([0]), ([1]))\n" +\
"Components: \n" +\
" [0]: <Sersic2D(amplitude=1., r_eff=1., n=4., x_0=0., y_0=0., ellip=0., theta=0.)>\n" +\
"\n" +\
" [1]: <Gaussian2D(amplitude=1., x_mean=0., y_mean=0., x_stddev=1., y_stddev=1., theta=0.)>\n" +\
"Parameters:\n" +\
" amplitude_0 r_eff_0 n_0 x_0_0 y_0_0 ... y_mean_1 x_stddev_1 y_stddev_1 theta_1\n" +\
" ----------- ------- --- ----- ----- ... -------- ---------- ---------- -------\n" +\
" 1.0 1.0 4.0 0.0 0.0 ... 0.0 1.0 1.0 0.0"
def _chisq_xys(p,
image,
psfmodel,
psffreeparams,
params_range,
model_maxlim=np.inf,
neg_penal=1.,
galmodel=None,
galconvpsf=False,
largechisq=1.e10):
""" calculate chisq. Return inf if psfmodel max is larter than model_maxlim. """
# organize params and params_range
psfparams_dict = dict(zip(psffreeparams, p[0:len(psffreeparams)]))
if galmodel is None:
params_dict = psfparams_dict
else:
params_dict = dict(
zip(psffreeparams + list(galmodel.param_names), p))
galmodel.parameters = np.array(p[len(psffreeparams):])
for key in galmodel.bounds:
if key not in params_range:
params_range.update({key: galmodel.bounds[key]})
if not _params_in_range(params_dict, params_range):
return largechisq
# transform psf
psfmodel_new = self._get_shifted_scaled_smoothed_resampled_model(
psfmodel, **psfparams_dict)
assert psfmodel_new.pixsize == image.pixsize
# evaluate galaxy model
if galmodel is None:
modeldata = psfmodel_new.data
else:
if galconvpsf: # convolve the 2d gaussian bestfit to psf
if 'background' in params_dict:
background = params_dict['background']
else:
background = 0.
try:
psf_normed = psfmodel_new.data - background
psf_normed = psf_normed / psf_normed.max()
gaussmodel = self._get_bestfit_gaussian_model(
psf_normed, boxsize=16)
galmodel_conv = ac.convolve_models(
galmodel,
gaussmodel,
normalize_kernel=True,
normalization_zero_tol=1e-8)
galdata = self._evaluate_model(galmodel_conv)
except:
print(
'Convolving galaxy with gaussian PSF failed, substituting PSF with default profile. '
)
gaussmodel = am.models.Gaussian2D(amplitude=1.,
x_mean=0.,
y_mean=0.,
x_stddev=1.,
y_stddev=1.,
theta=0.)
galmodel_conv = ac.convolve_models(
galmodel,
gaussmodel,
normalize_kernel=True,
normalization_zero_tol=1e-8)
galdata = self._evaluate_model(galmodel_conv)
else:
galdata = self._evaluate_model(galmodel)
modeldata = psfmodel_new.data + galdata
chisq = _calc_chisq(imgdata=image.data,
modeldata=modeldata,
model_maxlim=model_maxlim,
neg_penal=neg_penal)
return chisq