class TestImageModel(object):
"""
tests the source model routines
"""
def setup(self):
# data specifics
sigma_bkg = 0.01 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 100 # cutout pixel size
deltaPix = 0.05 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.3 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
data_class = Data(kwargs_data)
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid, y_grid, amp=1., sigma_x=sigma, sigma_y=sigma,
center_x=0, center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
self.kwargs_psf = {'psf_type': 'PIXEL', 'kernel_point_source': kernel_point_source}
psf_class = PSF(kwargs_psf=self.kwargs_psf)
# 'EXERNAL_SHEAR': external shear
kwargs_shear = {'e1': 0.01, 'e2': 0.01} # gamma_ext: shear strength, psi_ext: shear angel (in radian)
phi, q = 0.2, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_spemd = {'theta_E': 1., 'gamma': 1.8, 'center_x': 0, 'center_y': 0, 'e1': e1, 'e2': e2}
lens_model_list = ['SPEP', 'SHEAR']
self.kwargs_lens = [kwargs_spemd, kwargs_shear]
lens_model_class = LensModel(lens_model_list=lens_model_list)
# list of light profiles (for lens and source)
# 'SERSIC': spherical Sersic profile
kwargs_sersic = {'amp': 1., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
phi, q = 0.2, 0.9
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_sersic_ellipse = {'amp': 1., 'R_sersic': .6, 'n_sersic': 7, 'center_x': 0, 'center_y': 0,
'e1': e1, 'e2': e2}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
self.kwargs_ps = [{'ra_source': 0.0, 'dec_source': 0.0,
'source_amp': 10.}] # quasar point source position in the source plane and intrinsic brightness
point_source_class = PointSource(point_source_type_list=['SOURCE_POSITION'], fixed_magnification_list=[True])
kwargs_numerics = {'subgrid_res': 3, 'psf_subgrid': True}
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class,
point_source_class, kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps)
data_class.update_data(image_sim)
self.imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class,
point_source_class, kwargs_numerics=kwargs_numerics)
self.psf_fitting = PsfFitting(self.imageModel)
def test_update_psf(self):
fwhm = 0.5
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid, y_grid, amp=1., sigma_x=sigma, sigma_y=sigma,
center_x=0, center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
kwargs_psf = {'psf_type': 'PIXEL', 'kernel_point_source': kernel_point_source}
kwargs_psf_iter = {'stacking_method': 'median'}
kwargs_psf_return, improved_bool, error_map = self.psf_fitting.update_psf(kwargs_psf, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps, **kwargs_psf_iter)
assert improved_bool
kernel_new = kwargs_psf_return['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true) ** 2)
diff_new = np.sum((kernel_new - kernel_true) ** 2)
assert diff_old > diff_new
def test_update_iterative(self):
fwhm = 0.5
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid, y_grid, amp=1., sigma_x=sigma, sigma_y=sigma,
center_x=0, center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
kwargs_psf = {'psf_type': 'PIXEL', 'kernel_point_source': kernel_point_source}
kwargs_psf_iter = {'stacking_method': 'median'}
kwargs_psf_new = self.psf_fitting.update_iterative(kwargs_psf, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps,
**kwargs_psf_iter)
kernel_new = kwargs_psf_new['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true) ** 2)
diff_new = np.sum((kernel_new - kernel_true) ** 2)
assert diff_old > diff_new
assert diff_new < 0.01
kwargs_psf_new = self.psf_fitting.update_iterative(kwargs_psf, self.kwargs_lens, self.kwargs_source,
self.kwargs_lens_light, self.kwargs_ps, num_iter=3,
no_break=True)
kernel_new = kwargs_psf_new['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true) ** 2)
diff_new = np.sum((kernel_new - kernel_true) ** 2)
assert diff_old > diff_new
assert diff_new < 0.01
def test_mask_point_source(self):
ra_image, dec_image, amp = self.imageModel.PointSource.point_source_list(self.kwargs_ps, self.kwargs_lens)
print(ra_image, dec_image, amp)
x_grid, y_grid = self.imageModel.Data.coordinates
x_grid = util.image2array(x_grid)
y_grid = util.image2array(y_grid)
radius = 0.5
mask_point_source = self.psf_fitting.mask_point_source(ra_image, dec_image, x_grid, y_grid, radius, i=0)
assert mask_point_source[10, 10] == 1
class TestPSFIterationOld(object):
"""
tests the source model routines
"""
def setup(self):
# data specifics
sigma_bkg = 0.01 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 100 # cutout pixel size
deltaPix = 0.05 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.3 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
data_class = ImageData(**kwargs_data)
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid,
y_grid,
amp=1.,
sigma=sigma,
center_x=0,
center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
psf_error_map = np.zeros_like(kernel_point_source)
self.kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': kernel_point_source,
'psf_error_map': psf_error_map
}
psf_class = PSF(**self.kwargs_psf)
# 'EXERNAL_SHEAR': external shear
kwargs_shear = {
'gamma1': 0.01,
'gamma2': 0.01
} # gamma_ext: shear strength, psi_ext: shear angel (in radian)
phi, q = 0.2, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}
lens_model_list = ['SPEP', 'SHEAR']
self.kwargs_lens = [kwargs_spemd, kwargs_shear]
lens_model_class = LensModel(lens_model_list=lens_model_list)
# list of light profiles (for lens and source)
# 'SERSIC': spherical Sersic profile
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
phi, q = 0.2, 0.9
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 7,
'center_x': 0,
'center_y': 0,
'e1': e1,
'e2': e2
}
lens_light_model_list = ['SERSIC']
self.kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
self.kwargs_ps = [
{
'ra_source': 0.0,
'dec_source': 0.0,
'source_amp': 10.
}
] # quasar point source position in the source plane and intrinsic brightness
point_source_class = PointSource(
point_source_type_list=['SOURCE_POSITION'],
fixed_magnification_list=[True])
kwargs_numerics = {
'supersampling_factor': 3,
'supersampling_convolution': False,
'compute_mode': 'regular',
'point_source_supersampling_factor': 3
}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light,
self.kwargs_ps)
data_class.update_data(image_sim)
self.imageModel = ImageLinearFit(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
point_source_class,
kwargs_numerics=kwargs_numerics)
self.psf_fitting = PsfFitting(self.imageModel)
self.kwargs_params = {
'kwargs_lens': self.kwargs_lens,
'kwargs_source': self.kwargs_source,
'kwargs_lens_light': self.kwargs_lens_light,
'kwargs_ps': self.kwargs_ps
}
def test_update_psf(self):
fwhm = 0.5
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid,
y_grid,
amp=1.,
sigma=sigma,
center_x=0,
center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': kernel_point_source
}
kwargs_psf_iter = {
'stacking_method': 'median',
'error_map_radius': 0.5,
'new_procedure': False
}
kwargs_psf_return, improved_bool, error_map = self.psf_fitting.update_psf(
kwargs_psf, self.kwargs_params, **kwargs_psf_iter)
assert improved_bool
kernel_new = kwargs_psf_return['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true)**2)
diff_new = np.sum((kernel_new - kernel_true)**2)
assert diff_old > diff_new
def test_update_iterative(self):
fwhm = 0.5
sigma = util.fwhm2sigma(fwhm)
x_grid, y_grid = util.make_grid(numPix=31, deltapix=0.05)
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
gaussian = Gaussian()
kernel_point_source = gaussian.function(x_grid,
y_grid,
amp=1.,
sigma=sigma,
center_x=0,
center_y=0)
kernel_point_source /= np.sum(kernel_point_source)
kernel_point_source = util.array2image(kernel_point_source)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': kernel_point_source,
'kernel_point_source_init': kernel_point_source
}
kwargs_psf_iter = {
'stacking_method': 'median',
'psf_symmetry': 2,
'psf_iter_factor': 0.2,
'block_center_neighbour': 0.1,
'error_map_radius': 0.5,
'new_procedure': False,
'no_break': False,
'verbose': True,
'keep_psf_error_map': False
}
kwargs_params = copy.deepcopy(self.kwargs_params)
kwargs_ps = kwargs_params['kwargs_ps']
del kwargs_ps[0]['source_amp']
print(kwargs_params['kwargs_ps'])
kwargs_psf_new = self.psf_fitting.update_iterative(
kwargs_psf, kwargs_params, **kwargs_psf_iter)
kernel_new = kwargs_psf_new['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true)**2)
diff_new = np.sum((kernel_new - kernel_true)**2)
assert diff_old > diff_new
assert diff_new < 0.01
assert 'psf_error_map' in kwargs_psf_new
kwargs_psf_new = self.psf_fitting.update_iterative(
kwargs_psf,
kwargs_params,
num_iter=3,
no_break=True,
keep_psf_error_map=True)
kernel_new = kwargs_psf_new['kernel_point_source']
kernel_true = self.kwargs_psf['kernel_point_source']
kernel_old = kwargs_psf['kernel_point_source']
diff_old = np.sum((kernel_old - kernel_true)**2)
diff_new = np.sum((kernel_new - kernel_true)**2)
assert diff_old > diff_new
assert diff_new < 0.01