class TestLightModel(object):
"""
tests the source model routines
"""
def setup(self):
self.light_model_list = ['GAUSSIAN', 'MULTI_GAUSSIAN', 'SERSIC', 'SERSIC_ELLIPSE', 'DOUBLE_SERSIC',
'CORE_SERSIC', 'DOUBLE_CORE_SERSIC', 'BULDGE_DISK', 'SHAPELETS', 'HERNQUIST',
'HERNQUIST_ELLIPSE', 'PJAFFE', 'PJAFFE_ELLIPSE', 'UNIFORM', 'NONE'
]
self.kwargs = [
{'amp': 1., 'sigma_x': 1, 'sigma_y': 1., 'center_x': 0, 'center_y': 0}, # 'GAUSSIAN'
{'amp': [1., 2], 'sigma': [1, 3], 'center_x': 0, 'center_y': 0}, # 'MULTI_GAUSSIAN'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'center_x': 0, 'center_y': 0}, # 'SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0}, # 'SERSIC_ELLIPSE'
{'I0_sersic': 1, 'R_sersic': 0.5, 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0,
'I0_2': 1, 'R_2': 0.05, 'n_2': 2, 'phi_G_2': 0, 'q_2': 1}, # 'DOUBLE_SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'Re': 0.1, 'gamma': 2., 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5, 'center_x': 0, 'center_y': 0},
# 'CORE_SERSIC'
{'I0_sersic': 1, 'R_sersic': 0.5, 'Re': 0.1, 'gamma': 2., 'n_sersic': 1, 'q': 0.8, 'phi_G': 0.5,
'center_x': 0, 'center_y': 0, 'I0_2': 1, 'R_2': 0.05, 'n_2': 2, 'phi_G_2': 0, 'q_2': 1},
# 'DOUBLE_CORE_SERSIC'
{'I0_b': 1, 'R_b': 0.1, 'phi_G_b': 0, 'q_b': 1, 'I0_d': 2, 'R_d': 1, 'phi_G_d': 0.5, 'q_d': 0.7, 'center_x': 0, 'center_y': 0}, # BULDGE_DISK
{'amp': [1, 1, 1], 'beta': 0.5, 'n_max': 1, 'center_x': 0, 'center_y': 0}, # 'SHAPELETS'
{'sigma0': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0}, # 'HERNQUIST'
{'sigma0': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0, 'q': 0.8, 'phi_G': 0}, # 'HERNQUIST_ELLIPSE'
{'sigma0': 1, 'Ra': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0}, # 'PJAFFE'
{'sigma0': 1, 'Ra': 1, 'Rs': 0.5, 'center_x': 0, 'center_y': 0, 'q': 0.8, 'phi_G': 0}, # 'PJAFFE_ELLIPSE'
{'mean': 1}, # 'UNIFORM'
{}]# 'NONE'
self.LightModel = LightModel(light_model_list=self.light_model_list)
def test_init(self):
model_list = ['CORE_SERSIC', 'DOUBLE_CORE_SERSIC', 'BULDGE_DISK', 'SHAPELETS', 'UNIFORM']
lightModel = LightModel(light_model_list=model_list)
assert len(lightModel.profile_type_list) == len(model_list)
def test_surface_brightness(self):
output = self.LightModel.surface_brightness(x=1, y=1, kwargs_list=self.kwargs)
npt.assert_almost_equal(output, 2.544428612985992, decimal=6)
def test_surface_brightness_array(self):
output = self.LightModel.surface_brightness(x=[1], y=[1], kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0], 2.544428612985992, decimal=6)
def test_functions_split(self):
output = self.LightModel.functions_split(x=1., y=1., kwargs_list=self.kwargs)
assert output[0][0] == 0.058549831524319168
def test_re_normalize_flux(self):
kwargs_out = self.LightModel.re_normalize_flux(kwargs_list=self.kwargs, norm_factor=2)
assert kwargs_out[0]['amp'] == 2 * self.kwargs[0]['amp']
def test_raise(self):
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['WRONG'])
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['UNIFORM'])
lighModel.light_3d(r=1, kwargs_list=[{'amp': 1}])
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['UNIFORM'])
lighModel.profile_type_list = ['WRONG']
lighModel.functions_split(x=0, y=0, kwargs_list=[{}])
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['UNIFORM'])
lighModel.profile_type_list = ['WRONG']
lighModel.num_param_linear(kwargs_list=[{}])
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['UNIFORM'])
lighModel.profile_type_list = ['WRONG']
lighModel.update_linear(param=[1], i=0, kwargs_list=[{}])
with self.assertRaises(ValueError):
lighModel = LightModel(light_model_list=['UNIFORM'])
lighModel.profile_type_list = ['WRONG']
lighModel.total_flux(kwargs_list=[{}])
class TestLightModel(object):
"""
tests the source model routines
"""
def setup(self):
self.light_model_list = [
'GAUSSIAN', 'MULTI_GAUSSIAN', 'SERSIC', 'SERSIC_ELLIPSE',
'CORE_SERSIC', 'SHAPELETS', 'HERNQUIST', 'HERNQUIST_ELLIPSE',
'PJAFFE', 'PJAFFE_ELLIPSE', 'UNIFORM', 'POWER_LAW', 'NIE',
'INTERPOL', 'SHAPELETS_POLAR_EXP', 'ELLIPSOID'
]
phi_G, q = 0.5, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
self.kwargs = [
{
'amp': 1.,
'sigma': 1.,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN'
{
'amp': [1., 2],
'sigma': [1, 3],
'center_x': 0,
'center_y': 0
}, # 'MULTI_GAUSSIAN'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'center_x': 0,
'center_y': 0
}, # 'SERSIC'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'SERSIC_ELLIPSE'
{
'amp': 1,
'R_sersic': 0.5,
'Rb': 0.1,
'gamma': 2.,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
},
# 'CORE_SERSIC'
{
'amp': [1, 1, 1],
'beta': 0.5,
'n_max': 1,
'center_x': 0,
'center_y': 0
}, # 'SHAPELETS'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'HERNQUIST'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'HERNQUIST_ELLIPSE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'PJAFFE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'PJAFFE_ELLIPSE'
{
'amp': 1
}, # 'UNIFORM'
{
'amp': 1.,
'gamma': 2.,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'POWER_LAW'
{
'amp': .001,
'e1': 0,
'e2': 1.,
'center_x': 0,
'center_y': 0,
's_scale': 1.
}, # 'NIE'
{
'image': np.zeros((20, 5)),
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
},
{
'amp': [1],
'n_max': 0,
'beta': 1,
'center_x': 0,
'center_y': 0
},
{
'amp': 1,
'radius': 1.,
'e1': 0,
'e2': 0.1,
'center_x': 0,
'center_y': 0
} # 'ELLIPSOID'
]
self.LightModel = LightModel(light_model_list=self.light_model_list)
def test_init(self):
model_list = [
'CORE_SERSIC', 'SHAPELETS', 'SHAPELETS_POLAR',
'SHAPELETS_POLAR_EXP', 'UNIFORM', 'CHAMELEON', 'DOUBLE_CHAMELEON',
'TRIPLE_CHAMELEON'
]
lightModel = LightModel(light_model_list=model_list)
assert len(lightModel.profile_type_list) == len(model_list)
def test_surface_brightness(self):
output = self.LightModel.surface_brightness(x=1.,
y=1.,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output, 2.5886852663397137, decimal=6)
def test_surface_brightness_array(self):
output = self.LightModel.surface_brightness(x=[1],
y=[1],
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0], 2.5886852663397137, decimal=6)
def test_functions_split(self):
output = self.LightModel.functions_split(x=1.,
y=1.,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0][0], 0.058549831524319168, decimal=6)
def test_param_name_list(self):
param_name_list = self.LightModel.param_name_list
assert len(self.light_model_list) == len(param_name_list)
def test_num_param_linear(self):
num = self.LightModel.num_param_linear(self.kwargs, list_return=False)
assert num == 19
num_list = self.LightModel.num_param_linear(self.kwargs,
list_return=True)
assert num_list[0] == 1
def test_update_linear(self):
response, n = self.LightModel.functions_split(1, 1, self.kwargs)
param = np.ones(n) * 2
kwargs_out, i = self.LightModel.update_linear(param,
i=0,
kwargs_list=self.kwargs)
assert i == n
assert kwargs_out[0]['amp'] == 2
def test_total_flux(self):
light_model_list = [
'SERSIC', 'SERSIC_ELLIPSE', 'INTERPOL', 'GAUSSIAN',
'GAUSSIAN_ELLIPSE', 'MULTI_GAUSSIAN', 'MULTI_GAUSSIAN_ELLIPSE'
]
kwargs_list = [
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'center_x': 0,
'center_y': 0
}, # 'SERSIC'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
}, # 'SERSIC_ELLIPSE'
{
'image': np.ones((20, 5)),
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
}, # 'INTERPOL'
{
'amp': 2,
'sigma': 2,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN'
{
'amp': 2,
'sigma': 2,
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN_ELLIPSE'
{
'amp': [1, 1],
'sigma': [2, 1],
'center_x': 0,
'center_y': 0
}, # 'MULTI_GAUSSIAN'
{
'amp': [1, 1],
'sigma': [2, 1],
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
} # 'MULTI_GAUSSIAN_ELLIPSE'
]
lightModel = LightModel(light_model_list=light_model_list)
total_flux_list = lightModel.total_flux(kwargs_list)
assert total_flux_list[2] == 100
assert total_flux_list[3] == 2
assert total_flux_list[4] == 2
assert total_flux_list[5] == 2
assert total_flux_list[6] == 2
total_flux_list = lightModel.total_flux(kwargs_list, norm=True)
assert total_flux_list[2] == 100
assert total_flux_list[3] == 1
assert total_flux_list[4] == 1
assert total_flux_list[5] == 2
assert total_flux_list[6] == 2
def test_delete_interpol_caches(self):
x, y = util.make_grid(numPix=20, deltapix=1.)
gauss = Gaussian()
flux = gauss.function(x, y, amp=1., center_x=0., center_y=0., sigma=1.)
image = util.array2image(flux)
light_model_list = ['INTERPOL', 'INTERPOL']
kwargs_list = [{
'image': image,
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
}, {
'image': image,
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
}]
lightModel = LightModel(light_model_list=light_model_list)
output = lightModel.surface_brightness(x, y, kwargs_list)
for func in lightModel.func_list:
assert hasattr(func, '_image_interp')
lightModel.delete_interpol_caches()
for func in lightModel.func_list:
assert not hasattr(func, '_image_interp')
def test_check_positive_flux_profile(self):
ligthModel = LightModel(light_model_list=['GAUSSIAN'])
kwargs_list = [{'amp': 0, 'sigma': 1}]
bool = ligthModel.check_positive_flux_profile(kwargs_list)
assert bool
kwargs_list = [{'amp': -1, 'sigma': 1}]
bool = ligthModel.check_positive_flux_profile(kwargs_list)
assert not bool
class TestLightModel(object):
"""
tests the source model routines
"""
def setup(self):
self.light_model_list = [
'GAUSSIAN', 'MULTI_GAUSSIAN', 'SERSIC', 'SERSIC_ELLIPSE',
'CORE_SERSIC', 'SHAPELETS', 'HERNQUIST', 'HERNQUIST_ELLIPSE',
'PJAFFE', 'PJAFFE_ELLIPSE', 'UNIFORM', 'POWER_LAW', 'NIE',
'INTERPOL', 'SHAPELETS_POLAR_EXP'
]
phi_G, q = 0.5, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
self.kwargs = [
{
'amp': 1.,
'sigma_x': 1,
'sigma_y': 1.,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN'
{
'amp': [1., 2],
'sigma': [1, 3],
'center_x': 0,
'center_y': 0
}, # 'MULTI_GAUSSIAN'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'center_x': 0,
'center_y': 0
}, # 'SERSIC'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'SERSIC_ELLIPSE'
{
'amp': 1,
'R_sersic': 0.5,
'Re': 0.1,
'gamma': 2.,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
},
# 'CORE_SERSIC'
{
'amp': [1, 1, 1],
'beta': 0.5,
'n_max': 1,
'center_x': 0,
'center_y': 0
}, # 'SHAPELETS'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'HERNQUIST'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'HERNQUIST_ELLIPSE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'PJAFFE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'PJAFFE_ELLIPSE'
{
'amp': 1
}, # 'UNIFORM'
{
'amp': 1.,
'gamma': 2.,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'POWER_LAW'
{
'amp': .001,
'e1': 0,
'e2': 1.,
'center_x': 0,
'center_y': 0,
's_scale': 1.
}, # 'NIE'
{
'image': np.zeros((10, 10)),
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
},
{
'amp': [1],
'n_max': 0,
'beta': 1,
'center_x': 0,
'center_y': 0
}
]
self.LightModel = LightModel(light_model_list=self.light_model_list)
def test_init(self):
model_list = [
'CORE_SERSIC', 'SHAPELETS', 'SHAPELETS_POLAR',
'SHAPELETS_POLAR_EXP', 'UNIFORM', 'CHAMELEON', 'DOUBLE_CHAMELEON',
'TRIPLE_CHAMELEON'
]
lightModel = LightModel(light_model_list=model_list)
assert len(lightModel.profile_type_list) == len(model_list)
def test_surface_brightness(self):
output = self.LightModel.surface_brightness(x=1,
y=1,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output, 3.7065728131855824, decimal=6)
def test_surface_brightness_array(self):
output = self.LightModel.surface_brightness(x=[1],
y=[1],
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0], 3.7065728131855824, decimal=6)
def test_functions_split(self):
output = self.LightModel.functions_split(x=1.,
y=1.,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0][0], 0.058549831524319168, decimal=6)
def test_re_normalize_flux(self):
kwargs_out = self.LightModel.re_normalize_flux(kwargs_list=self.kwargs,
norm_factor=2)
assert kwargs_out[0]['amp'] == 2 * self.kwargs[0]['amp']
def test_param_name_list(self):
param_name_list = self.LightModel.param_name_list()
assert len(self.light_model_list) == len(param_name_list)
def test_num_param_linear(self):
num = self.LightModel.num_param_linear(self.kwargs, list_return=False)
assert num == 18
num_list = self.LightModel.num_param_linear(self.kwargs,
list_return=True)
assert num_list[0] == 1
def test_update_linear(self):
response, n = self.LightModel.functions_split(1, 1, self.kwargs)
param = np.ones(n) * 2
kwargs_out, i = self.LightModel.update_linear(param,
i=0,
kwargs_list=self.kwargs)
assert i == n
assert kwargs_out[0]['amp'] == 2
def test_total_flux(self):
light_model_list = [
'SERSIC', 'SERSIC_ELLIPSE', 'INTERPOL', 'GAUSSIAN',
'GAUSSIAN_ELLIPSE', 'MULTI_GAUSSIAN', 'MULTI_GAUSSIAN_ELLIPSE'
]
kwargs_list = [
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'center_x': 0,
'center_y': 0
}, # 'SERSIC'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
}, # 'SERSIC_ELLIPSE'
{
'image': np.ones((10, 10)),
'scale': 1,
'phi_G': 0,
'center_x': 0,
'center_y': 0
}, # 'INTERPOL'
{
'amp': 2,
'sigma_x': 2,
'sigma_y': 1,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN'
{
'amp': 2,
'sigma': 2,
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN_ELLIPSE'
{
'amp': [1, 1],
'sigma': [2, 1],
'center_x': 0,
'center_y': 0
}, # 'MULTI_GAUSSIAN'
{
'amp': [1, 1],
'sigma': [2, 1],
'e1': 0.1,
'e2': 0,
'center_x': 0,
'center_y': 0
} # 'MULTI_GAUSSIAN_ELLIPSE'
]
lightModel = LightModel(light_model_list=light_model_list)
total_flux_list = lightModel.total_flux(kwargs_list)
assert total_flux_list[2] == 100
assert total_flux_list[3] == 2
assert total_flux_list[4] == 2
assert total_flux_list[5] == 2
assert total_flux_list[6] == 2
total_flux_list = lightModel.total_flux(kwargs_list, norm=True)
assert total_flux_list[2] == 100
assert total_flux_list[3] == 1
assert total_flux_list[4] == 1
assert total_flux_list[5] == 2
assert total_flux_list[6] == 2
class LensAnalysis(object):
"""
class to compute flux ratio anomalies, inherited from standard MakeImage
"""
def __init__(self, kwargs_model):
self.LensLightModel = LightModel(
kwargs_model.get('lens_light_model_list', []))
self.SourceModel = LightModel(
kwargs_model.get('source_light_model_list', []))
self.LensModel = LensModel(
lens_model_list=kwargs_model.get('lens_model_list', []),
z_source=kwargs_model.get('z_source', None),
redshift_list=kwargs_model.get('redshift_list', None),
multi_plane=kwargs_model.get('multi_plane', False))
self._lensModelExtensions = LensModelExtensions(self.LensModel)
self.PointSource = PointSource(point_source_type_list=kwargs_model.get(
'point_source_model_list', []))
self.kwargs_model = kwargs_model
self.NumLensModel = NumericLens(
lens_model_list=kwargs_model.get('lens_model_list', []))
def fermat_potential(self, kwargs_lens, kwargs_ps):
ra_pos, dec_pos = self.PointSource.image_position(
kwargs_ps, kwargs_lens)
ra_pos = ra_pos[0]
dec_pos = dec_pos[0]
ra_source, dec_source = self.LensModel.ray_shooting(
ra_pos, dec_pos, kwargs_lens)
ra_source = np.mean(ra_source)
dec_source = np.mean(dec_source)
fermat_pot = self.LensModel.fermat_potential(ra_pos, dec_pos,
ra_source, dec_source,
kwargs_lens)
return fermat_pot
def ellipticity_lens_light(self,
kwargs_lens_light,
center_x=0,
center_y=0,
model_bool_list=None,
deltaPix=None,
numPix=None):
"""
make sure that the window covers all the light, otherwise the moments may give to low answers.
:param kwargs_lens_light:
:param center_x:
:param center_y:
:param model_bool_list:
:param deltaPix:
:param numPix:
:return:
"""
if model_bool_list is None:
model_bool_list = [True] * len(kwargs_lens_light)
if numPix is None:
numPix = 100
if deltaPix is None:
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
I_xy = self._lens_light_internal(x_grid,
y_grid,
kwargs_lens_light,
model_bool_list=model_bool_list)
e1, e2 = analysis_util.ellipticities(I_xy, x_grid, y_grid)
return e1, e2
def half_light_radius_lens(self,
kwargs_lens_light,
center_x=0,
center_y=0,
model_bool_list=None,
deltaPix=None,
numPix=None):
"""
computes numerically the half-light-radius of the deflector light and the total photon flux
:param kwargs_lens_light:
:return:
"""
if model_bool_list is None:
model_bool_list = [True] * len(kwargs_lens_light)
if numPix is None:
numPix = 1000
if deltaPix is None:
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
lens_light = self._lens_light_internal(x_grid,
y_grid,
kwargs_lens_light,
model_bool_list=model_bool_list)
R_h = analysis_util.half_light_radius(lens_light, x_grid, y_grid,
center_x, center_y)
return R_h
def half_light_radius_source(self,
kwargs_source,
center_x=0,
center_y=0,
deltaPix=None,
numPix=None):
"""
computes numerically the half-light-radius of the deflector light and the total photon flux
:param kwargs_source:
:return:
"""
if numPix is None:
numPix = 1000
if deltaPix is None:
deltaPix = 0.005
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
source_light = self.SourceModel.surface_brightness(
x_grid, y_grid, kwargs_source)
R_h = analysis_util.half_light_radius(source_light,
x_grid,
y_grid,
center_x=center_x,
center_y=center_y)
return R_h
def _lens_light_internal(self,
x_grid,
y_grid,
kwargs_lens_light,
model_bool_list=None):
"""
evaluates only part of the light profiles
:param x_grid:
:param y_grid:
:param kwargs_lens_light:
:return:
"""
if model_bool_list is None:
model_bool_list = [True] * len(kwargs_lens_light)
lens_light = np.zeros_like(x_grid)
for i, bool in enumerate(model_bool_list):
if bool is True:
lens_light_i = self.LensLightModel.surface_brightness(
x_grid, y_grid, kwargs_lens_light, k=i)
lens_light += lens_light_i
return lens_light
def multi_gaussian_lens_light(self,
kwargs_lens_light,
model_bool_list=None,
e1=0,
e2=0,
n_comp=20,
deltaPix=None,
numPix=None):
"""
multi-gaussian decomposition of the lens light profile (in 1-dimension)
:param kwargs_lens_light:
:param n_comp:
:return:
"""
if 'center_x' in kwargs_lens_light[0]:
center_x = kwargs_lens_light[0]['center_x']
center_y = kwargs_lens_light[0]['center_y']
else:
center_x, center_y = 0, 0
r_h = self.half_light_radius_lens(kwargs_lens_light,
center_x=center_x,
center_y=center_y,
model_bool_list=model_bool_list,
deltaPix=deltaPix,
numPix=numPix)
r_array = np.logspace(-3, 2, 200) * r_h * 2
x_coords, y_coords = param_util.transform_e1e2(r_array,
np.zeros_like(r_array),
e1=-e1,
e2=-e2)
x_coords += center_x
y_coords += center_y
#r_array = np.logspace(-2, 1, 50) * r_h
flux_r = self._lens_light_internal(x_coords,
y_coords,
kwargs_lens_light,
model_bool_list=model_bool_list)
amplitudes, sigmas, norm = mge.mge_1d(r_array, flux_r, N=n_comp)
return amplitudes, sigmas, center_x, center_y
def multi_gaussian_lens(self,
kwargs_lens,
model_bool_list=None,
e1=0,
e2=0,
n_comp=20):
"""
multi-gaussian lens model in convergence space
:param kwargs_lens:
:param n_comp:
:return:
"""
if 'center_x' in kwargs_lens[0]:
center_x = kwargs_lens[0]['center_x']
center_y = kwargs_lens[0]['center_y']
else:
raise ValueError('no keyword center_x defined!')
theta_E = self._lensModelExtensions.effective_einstein_radius(
kwargs_lens)
r_array = np.logspace(-4, 2, 200) * theta_E
x_coords, y_coords = param_util.transform_e1e2(r_array,
np.zeros_like(r_array),
e1=-e1,
e2=-e2)
x_coords += center_x
y_coords += center_y
#r_array = np.logspace(-2, 1, 50) * theta_E
if model_bool_list is None:
model_bool_list = [True] * len(kwargs_lens)
kappa_s = np.zeros_like(r_array)
for i in range(len(kwargs_lens)):
if model_bool_list[i] is True:
kappa_s += self.LensModel.kappa(x_coords,
y_coords,
kwargs_lens,
k=i)
amplitudes, sigmas, norm = mge.mge_1d(r_array, kappa_s, N=n_comp)
return amplitudes, sigmas, center_x, center_y
def flux_components(self,
kwargs_light,
n_grid=400,
delta_grid=0.01,
deltaPix=0.05,
type="lens"):
"""
computes the total flux in each component of the model
:param kwargs_light:
:param n_grid:
:param delta_grid:
:return:
"""
flux_list = []
R_h_list = []
x_grid, y_grid = util.make_grid(numPix=n_grid, deltapix=delta_grid)
kwargs_copy = copy.deepcopy(kwargs_light)
for k, kwargs in enumerate(kwargs_light):
if 'center_x' in kwargs_copy[k]:
kwargs_copy[k]['center_x'] = 0
kwargs_copy[k]['center_y'] = 0
if type == 'lens':
light = self.LensLightModel.surface_brightness(x_grid,
y_grid,
kwargs_copy,
k=k)
elif type == 'source':
light = self.SourceModel.surface_brightness(x_grid,
y_grid,
kwargs_copy,
k=k)
else:
raise ValueError("type %s not supported!" % type)
flux = np.sum(light) * delta_grid**2 / deltaPix**2
R_h = analysis_util.half_light_radius(light, x_grid, y_grid)
flux_list.append(flux)
R_h_list.append(R_h)
return flux_list, R_h_list
def error_map_source(self, kwargs_source, x_grid, y_grid, cov_param):
"""
variance of the linear source reconstruction in the source plane coordinates,
computed by the diagonal elements of the covariance matrix of the source reconstruction as a sum of the errors
of the basis set.
:param kwargs_source: keyword arguments of source model
:param x_grid: x-axis of positions to compute error map
:param y_grid: y-axis of positions to compute error map
:param cov_param: covariance matrix of liner inversion parameters
:return: diagonal covariance errors at the positions (x_grid, y_grid)
"""
error_map = np.zeros_like(x_grid)
basis_functions, n_source = self.SourceModel.functions_split(
x_grid, y_grid, kwargs_source)
basis_functions = np.array(basis_functions)
if cov_param is not None:
for i in range(len(error_map)):
error_map[i] = basis_functions[:, i].T.dot(
cov_param[:n_source, :n_source]).dot(basis_functions[:, i])
return error_map
def light2mass_mge(self,
kwargs_lens_light,
model_bool_list=None,
elliptical=False,
numPix=100,
deltaPix=0.05):
# estimate center
if 'center_x' in kwargs_lens_light[0]:
center_x, center_y = kwargs_lens_light[0][
'center_x'], kwargs_lens_light[0]['center_y']
else:
center_x, center_y = 0, 0
# estimate half-light radius
r_h = self.half_light_radius_lens(kwargs_lens_light,
center_x=center_x,
center_y=center_y,
model_bool_list=model_bool_list,
numPix=numPix,
deltaPix=deltaPix)
# estimate ellipticity at half-light radius
if elliptical is True:
e1, e2 = self.ellipticity_lens_light(
kwargs_lens_light,
center_x=center_x,
center_y=center_y,
model_bool_list=model_bool_list,
deltaPix=deltaPix * 2,
numPix=numPix)
else:
e1, e2 = 0, 0
# MGE around major axis
amplitudes, sigmas, center_x, center_y = self.multi_gaussian_lens_light(
kwargs_lens_light,
model_bool_list=model_bool_list,
e1=e1,
e2=e2,
n_comp=20)
kwargs_mge = {
'amp': amplitudes,
'sigma': sigmas,
'center_x': center_x,
'center_y': center_y
}
if elliptical:
kwargs_mge['e1'] = e1
kwargs_mge['e2'] = e2
# rotate axes and add ellipticity to model kwargs
return kwargs_mge
@staticmethod
def light2mass_interpol(lens_light_model_list,
kwargs_lens_light,
numPix=100,
deltaPix=0.05,
subgrid_res=5,
center_x=0,
center_y=0):
"""
takes a lens light model and turns it numerically in a lens model
(with all lensmodel quantities computed on a grid). Then provides an interpolated grid for the quantities.
:param kwargs_lens_light: lens light keyword argument list
:param numPix: number of pixels per axis for the return interpolation
:param deltaPix: interpolation/pixel size
:param center_x: center of the grid
:param center_y: center of the grid
:param subgrid: subgrid for the numerical integrals
:return:
"""
# make sugrid
x_grid_sub, y_grid_sub = util.make_grid(numPix=numPix * 5,
deltapix=deltaPix,
subgrid_res=subgrid_res)
import lenstronomy.Util.mask as mask_util
mask = mask_util.mask_sphere(x_grid_sub,
y_grid_sub,
center_x,
center_y,
r=1)
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
# compute light on the subgrid
lightModel = LightModel(light_model_list=lens_light_model_list)
flux = lightModel.surface_brightness(x_grid_sub, y_grid_sub,
kwargs_lens_light)
flux_norm = np.sum(flux[mask == 1]) / np.sum(mask)
flux /= flux_norm
from lenstronomy.LensModel.numerical_profile_integrals import ConvergenceIntegrals
integral = ConvergenceIntegrals()
# compute lensing quantities with subgrid
convergence_sub = flux
f_x_sub, f_y_sub = integral.deflection_from_kappa(convergence_sub,
x_grid_sub,
y_grid_sub,
deltaPix=deltaPix /
float(subgrid_res))
f_sub = integral.potential_from_kappa(convergence_sub,
x_grid_sub,
y_grid_sub,
deltaPix=deltaPix /
float(subgrid_res))
# interpolation function on lensing quantities
x_axes_sub, y_axes_sub = util.get_axes(x_grid_sub, y_grid_sub)
from lenstronomy.LensModel.Profiles.interpol import Interpol_func
interp_func = Interpol_func()
interp_func.do_interp(x_axes_sub, y_axes_sub, f_sub, f_x_sub, f_y_sub)
# compute lensing quantities on sparser grid
x_axes, y_axes = util.get_axes(x_grid, y_grid)
f_ = interp_func.function(x_grid, y_grid)
f_x, f_y = interp_func.derivatives(x_grid, y_grid)
# numerical differentials for second order differentials
from lenstronomy.LensModel.numeric_lens_differentials import NumericLens
lens_differential = NumericLens(lens_model_list=['INTERPOL'])
kwargs = [{
'grid_interp_x': x_axes_sub,
'grid_interp_y': y_axes_sub,
'f_': f_sub,
'f_x': f_x_sub,
'f_y': f_y_sub
}]
f_xx, f_xy, f_yx, f_yy = lens_differential.hessian(
x_grid, y_grid, kwargs)
kwargs_interpol = {
'grid_interp_x': x_axes,
'grid_interp_y': y_axes,
'f_': util.array2image(f_),
'f_x': util.array2image(f_x),
'f_y': util.array2image(f_y),
'f_xx': util.array2image(f_xx),
'f_xy': util.array2image(f_xy),
'f_yy': util.array2image(f_yy)
}
return kwargs_interpol
def mass_fraction_within_radius(self,
kwargs_lens,
center_x,
center_y,
theta_E,
numPix=100):
"""
computes the mean convergence of all the different lens model components within a spherical aperture
:param kwargs_lens: lens model keyword argument list
:param center_x: center of the aperture
:param center_y: center of the aperture
:param theta_E: radius of aperture
:return: list of average convergences for all the model components
"""
x_grid, y_grid = util.make_grid(numPix=numPix,
deltapix=2. * theta_E / numPix)
x_grid += center_x
y_grid += center_y
mask = mask_util.mask_sphere(x_grid, y_grid, center_x, center_y,
theta_E)
kappa_list = []
for i in range(len(kwargs_lens)):
kappa = self.LensModel.kappa(x_grid, y_grid, kwargs_lens, k=i)
kappa_mean = np.sum(kappa * mask) / np.sum(mask)
kappa_list.append(kappa_mean)
return kappa_list
class TestLightModel(object):
"""
tests the source model routines
"""
def setup(self):
self.light_model_list = [
'GAUSSIAN', 'MULTI_GAUSSIAN', 'SERSIC', 'SERSIC_ELLIPSE',
'CORE_SERSIC', 'SHAPELETS', 'HERNQUIST', 'HERNQUIST_ELLIPSE',
'PJAFFE', 'PJAFFE_ELLIPSE', 'UNIFORM', 'POWER_LAW', 'NIE'
]
phi_G, q = 0.5, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
self.kwargs = [
{
'amp': 1.,
'sigma_x': 1,
'sigma_y': 1.,
'center_x': 0,
'center_y': 0
}, # 'GAUSSIAN'
{
'amp': [1., 2],
'sigma': [1, 3],
'center_x': 0,
'center_y': 0
}, # 'MULTI_GAUSSIAN'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'center_x': 0,
'center_y': 0
}, # 'SERSIC'
{
'amp': 1,
'R_sersic': 0.5,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'SERSIC_ELLIPSE'
{
'amp': 1,
'R_sersic': 0.5,
'Re': 0.1,
'gamma': 2.,
'n_sersic': 1,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
},
# 'CORE_SERSIC'
{
'amp': [1, 1, 1],
'beta': 0.5,
'n_max': 1,
'center_x': 0,
'center_y': 0
}, # 'SHAPELETS'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'HERNQUIST'
{
'amp': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'HERNQUIST_ELLIPSE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0
}, # 'PJAFFE'
{
'amp': 1,
'Ra': 1,
'Rs': 0.5,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}, # 'PJAFFE_ELLIPSE'
{
'amp': 1
}, # 'UNIFORM'
{
'amp': 1.,
'gamma': 2.,
'e1': e1,
'e2': e2,
'center_x': 0,
'center_y': 0
}, # 'POWER_LAW'
{
'amp': .001,
'e1': 0,
'e2': 1.,
'center_x': 0,
'center_y': 0,
's_scale': 1.
}, # 'NIE'
]
self.LightModel = LightModel(light_model_list=self.light_model_list)
def test_init(self):
model_list = [
'CORE_SERSIC', 'SHAPELETS', 'UNIFORM', 'CHAMELEON',
'DOUBLE_CHAMELEON'
]
lightModel = LightModel(light_model_list=model_list)
assert len(lightModel.profile_type_list) == len(model_list)
def test_surface_brightness(self):
output = self.LightModel.surface_brightness(x=1,
y=1,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output, 3.512593731652167, decimal=6)
def test_surface_brightness_array(self):
output = self.LightModel.surface_brightness(x=[1],
y=[1],
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0], 3.512593731652167, decimal=6)
def test_functions_split(self):
output = self.LightModel.functions_split(x=1.,
y=1.,
kwargs_list=self.kwargs)
npt.assert_almost_equal(output[0][0], 0.058549831524319168, decimal=6)
def test_re_normalize_flux(self):
kwargs_out = self.LightModel.re_normalize_flux(kwargs_list=self.kwargs,
norm_factor=2)
assert kwargs_out[0]['amp'] == 2 * self.kwargs[0]['amp']
def test_param_name_list(self):
param_name_list = self.LightModel.param_name_list()
assert len(self.light_model_list) == len(param_name_list)
def test_update_linear(self):
response, n = self.LightModel.functions_split(1, 1, self.kwargs)
param = np.ones(n) * 2
kwargs_out, i = self.LightModel.update_linear(param,
i=0,
kwargs_list=self.kwargs)
assert i == n
assert kwargs_out[0]['amp'] == 2
class LensAnalysis(object):
"""
class to compute flux ratio anomalies, inherited from standard MakeImage
"""
def __init__(self, kwargs_model):
self.LensLightModel = LightModel(kwargs_model.get('lens_light_model_list', ['NONE']))
self.SourceModel = LightModel(kwargs_model.get('source_light_model_list', ['NONE']))
self.LensModel = LensModelExtensions(lens_model_list=kwargs_model['lens_model_list'])
self.PointSource = PointSource(point_source_type_list=kwargs_model.get('point_source_model_list', ['NONE']))
self.kwargs_model = kwargs_model
self.NumLensModel = NumericLens(lens_model_list=kwargs_model['lens_model_list'])
self.gaussian = Gaussian()
def fermat_potential(self, kwargs_lens, kwargs_ps):
ra_pos, dec_pos = self.PointSource.image_position(kwargs_ps, kwargs_lens)
ra_pos = ra_pos[0]
dec_pos = dec_pos[0]
ra_source, dec_source = self.LensModel.ray_shooting(ra_pos, dec_pos, kwargs_lens)
ra_source = np.mean(ra_source)
dec_source = np.mean(dec_source)
fermat_pot = self.LensModel.fermat_potential(ra_pos, dec_pos, ra_source, dec_source, kwargs_lens)
return fermat_pot
def half_light_radius_lens(self, kwargs_lens_light, deltaPix=None, numPix=None):
"""
computes numerically the half-light-radius of the deflector light and the total photon flux
:param kwargs_lens_light:
:return:
"""
if numPix is None:
numPix = 1000
if deltaPix is None:
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
lens_light = self._lens_light_internal(x_grid, y_grid, kwargs_lens_light)
R_h = analysis_util.half_light_radius(lens_light, x_grid, y_grid)
return R_h
def half_light_radius_source(self, kwargs_source, deltaPix=None, numPix=None):
"""
computes numerically the half-light-radius of the deflector light and the total photon flux
:param kwargs_lens_light:
:return:
"""
if numPix is None:
numPix = 1000
if deltaPix is None:
deltaPix = 0.005
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
source_light = self.SourceModel.surface_brightness(x_grid, y_grid, kwargs_source)
R_h = analysis_util.half_light_radius(source_light, x_grid, y_grid, center_x=kwargs_source[0]['center_x'], center_y=kwargs_source[0]['center_y'])
return R_h
def _lens_light_internal(self, x_grid, y_grid, kwargs_lens_light):
"""
:param x_grid:
:param y_grid:
:param kwargs_lens_light:
:return:
"""
kwargs_lens_light_copy = copy.deepcopy(kwargs_lens_light)
lens_light_model_internal_bool = self.kwargs_model.get('light_model_deflector_bool', [True] * len(kwargs_lens_light))
lens_light = np.zeros_like(x_grid)
for i, bool in enumerate(lens_light_model_internal_bool):
if bool is True:
kwargs_lens_light_copy[i]['center_x'] = 0
kwargs_lens_light_copy[i]['center_y'] = 0
lens_light_i = self.LensLightModel.surface_brightness(x_grid, y_grid, kwargs_lens_light_copy, k=i)
lens_light += lens_light_i
return lens_light
def multi_gaussian_lens_light(self, kwargs_lens_light, n_comp=20):
"""
multi-gaussian decomposition of the lens light profile (in 1-dimension)
:param kwargs_lens_light:
:param n_comp:
:return:
"""
r_h = self.half_light_radius_lens(kwargs_lens_light)
r_array = np.logspace(-3, 2, 200) * r_h * 2
#r_array = np.logspace(-2, 1, 50) * r_h
flux_r = self._lens_light_internal(r_array, np.zeros_like(r_array), kwargs_lens_light)
amplitudes, sigmas, norm = mge.mge_1d(r_array, flux_r, N=n_comp)
return amplitudes, sigmas
def multi_gaussian_lens(self, kwargs_lens, n_comp=20):
"""
multi-gaussian lens model in convergence space
:param kwargs_lens:
:param n_comp:
:return:
"""
kwargs_lens_copy = copy.deepcopy(kwargs_lens)
if 'center_x' in kwargs_lens_copy[0]:
center_x = kwargs_lens_copy[0]['center_x']
center_y = kwargs_lens_copy[0]['center_y']
else:
raise ValueError('no keyword center_x defined!')
theta_E = self.LensModel.effective_einstein_radius(kwargs_lens)
r_array = np.logspace(-4, 2, 200) * theta_E
#r_array = np.logspace(-2, 1, 50) * theta_E
lens_model_internal_bool = self.kwargs_model.get('lens_model_internal_bool', [True] * len(kwargs_lens))
kappa_s = np.zeros_like(r_array)
for i in range(len(kwargs_lens_copy)):
if lens_model_internal_bool[i]:
if 'center_x' in kwargs_lens_copy[0]:
kwargs_lens_copy[i]['center_x'] -= center_x
kwargs_lens_copy[i]['center_y'] -= center_y
kappa_s += self.LensModel.kappa(r_array, np.zeros_like(r_array), kwargs_lens_copy, k=i)
amplitudes, sigmas, norm = mge.mge_1d(r_array, kappa_s, N=n_comp)
return amplitudes, sigmas, center_x, center_y
def flux_components(self, kwargs_light, n_grid=400, delta_grid=0.01, deltaPix=0.05, type="lens"):
"""
computes the total flux in each component of the model
:param kwargs_light:
:param n_grid:
:param delta_grid:
:return:
"""
flux_list = []
R_h_list = []
x_grid, y_grid = util.make_grid(numPix=n_grid, deltapix=delta_grid)
kwargs_copy = copy.deepcopy(kwargs_light)
for k, kwargs in enumerate(kwargs_light):
if 'center_x' in kwargs_copy[k]:
kwargs_copy[k]['center_x'] = 0
kwargs_copy[k]['center_y'] = 0
if type == 'lens':
light = self.LensLightModel.surface_brightness(x_grid, y_grid, kwargs_copy, k=k)
elif type == 'source':
light = self.SourceModel.surface_brightness(x_grid, y_grid, kwargs_copy, k=k)
else:
raise ValueError("type %s not supported!" % type)
flux = np.sum(light)*delta_grid**2/ deltaPix**2
R_h = analysis_util.half_light_radius(light, x_grid, y_grid)
flux_list.append(flux)
R_h_list.append(R_h)
return flux_list, R_h_list
@staticmethod
def buldge_disk_ratio(kwargs_buldge_disk):
"""
computes the buldge-to-disk ratio of the
:param kwargs_buldge_disk: kwargs of the buldge2disk function
:return:
"""
kwargs_bd = copy.deepcopy(kwargs_buldge_disk)
kwargs_bd['center_x'] = 0
kwargs_bd['center_y'] = 0
deltaPix = 0.05
numPix = 200
x_grid, y_grid = util.make_grid(numPix, deltaPix)
from lenstronomy.LightModel.Profiles.sersic import BuldgeDisk
bd_class = BuldgeDisk()
light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)
light_tot = np.sum(light_grid)
kwargs_bd['I0_d'] = 0
light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)
light_buldge = np.sum(light_grid)
return light_tot, light_buldge
def error_map_source(self, kwargs_source, x_grid, y_grid, cov_param):
"""
variance of the linear source reconstruction in the source plane coordinates,
computed by the diagonal elements of the covariance matrix of the source reconstruction as a sum of the errors
of the basis set.
:param kwargs_source: keyword arguments of source model
:param x_grid: x-axis of positions to compute error map
:param y_grid: y-axis of positions to compute error map
:param cov_param: covariance matrix of liner inversion parameters
:return: diagonal covariance errors at the positions (x_grid, y_grid)
"""
error_map = np.zeros_like(x_grid)
basis_functions, n_source = self.SourceModel.functions_split(x_grid, y_grid, kwargs_source)
basis_functions = np.array(basis_functions)
if cov_param is not None:
for i in range(len(error_map)):
error_map[i] = basis_functions[:, i].T.dot(cov_param[:n_source, :n_source]).dot(basis_functions[:, i])
return error_map
def test_raise(self):
with self.assertRaises(ValueError):
# try to set decomposition scale to higher than maximal value
starlets = SLIT_Starlets(force_no_pysap=True)
# define a test image with gaussian components
num_pix = 50
x, y = util.make_grid(num_pix, 1)
# build a non-trivial positive image from sum of gaussians
gaussian = Gaussian()
gaussian1 = gaussian.function(x,
y,
amp=100,
sigma=1,
center_x=-7,
center_y=-7)
gaussian2 = gaussian.function(x,
y,
amp=500,
sigma=3,
center_x=-3,
center_y=-3)
gaussian3 = gaussian.function(x,
y,
amp=2000,
sigma=5,
center_x=+5,
center_y=+5)
test_image = util.array2image(gaussian1 + gaussian2 + gaussian3)
n_scales = 100
_ = starlets.decomposition_2d(test_image, n_scales)
with self.assertRaises(ValueError):
# try to set decomposition scale to negative value
starlets = SLIT_Starlets(force_no_pysap=True)
# define a test image with gaussian components
num_pix = 50
x, y = util.make_grid(num_pix, 1)
# build a non-trivial positive image from sum of gaussians
gaussian = Gaussian()
gaussian1 = gaussian.function(x,
y,
amp=100,
sigma=1,
center_x=-7,
center_y=-7)
gaussian2 = gaussian.function(x,
y,
amp=500,
sigma=3,
center_x=-3,
center_y=-3)
gaussian3 = gaussian.function(x,
y,
amp=2000,
sigma=5,
center_x=+5,
center_y=+5)
test_image = util.array2image(gaussian1 + gaussian2 + gaussian3)
n_scales = -1
_ = starlets.decomposition_2d(test_image, n_scales)
with self.assertRaises(ValueError):
# function_split is not supported/defined for pixel-based profiles
light_model = LightModel(['SLIT_STARLETS'])
num_pix = 20
x, y = util.make_grid(num_pix, 1)
kwargs_list = [{
'amp': np.ones((3, num_pix, num_pix)),
'n_scales': 3,
'n_pixels': 20**2,
'center_x': 0,
'center_y': 0,
'scale': 1
}]
_ = light_model.functions_split(x, y, kwargs_list)
with self.assertRaises(ValueError):
# provided a wrong shape for starlet coefficients
starlet_class = SLIT_Starlets()
num_pix = 20
x, y = util.make_grid(num_pix, 1)
coeffs_wrong = np.ones((3, num_pix**2))
kwargs_list = {
'amp': coeffs_wrong,
'n_scales': 3,
'n_pixels': 20**2,
'center_x': 0,
'center_y': 0,
'scale': 1
}
_ = starlet_class.function(x, y, **kwargs_list)
image_wrong = np.ones((1, num_pix, num_pix))
_ = starlet_class.decomposition(image_wrong, 3)
with self.assertRaises(ValueError):
# provided a wrong shape for image to be decomposed
starlet_class = SLIT_Starlets()
num_pix = 20
image_wrong = np.ones((2, num_pix, num_pix))
_ = starlet_class.decomposition(image_wrong, 3)
