def psf_iteration(self, compute_bands=None, **kwargs_psf_iter):
"""
iterative PSF reconstruction
:param compute_bands: bool list, if multiple bands, this process can be limited to a subset of bands
:param kwargs_psf_iter: keyword arguments as used or available in PSFIteration.update_iterative() definition
:return: 0, updated PSF is stored in self.multi_band_list
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
likelihood_mask_list = kwargs_likelihood.get('image_likelihood_mask_list', None)
kwargs_pixelbased = kwargs_likelihood.get('kwargs_pixelbased', None)
kwargs_temp = self.best_fit(bijective=False)
if compute_bands is None:
compute_bands = [True] * len(self.multi_band_list)
for band_index in range(len(self.multi_band_list)):
if compute_bands[band_index] is True:
kwargs_psf = self.multi_band_list[band_index][1]
image_model = SingleBandMultiModel(self.multi_band_list, kwargs_model,
likelihood_mask_list=likelihood_mask_list, band_index=band_index,
kwargs_pixelbased=kwargs_pixelbased)
psf_iter = PsfFitting(image_model_class=image_model)
kwargs_psf = psf_iter.update_iterative(kwargs_psf, kwargs_params=kwargs_temp, **kwargs_psf_iter)
self.multi_band_list[band_index][1] = kwargs_psf
return 0
def psf_iteration(self, fitting_kwargs, lens_input, source_input,
lens_light_input, ps_input, cosmo_input):
#lens_temp = copy.deepcopy(lens_input)
lens_updated = self._param.update_lens_scaling(cosmo_input, lens_input)
source_updated = self._param.image2source_plane(
source_input, lens_updated)
psf_iter_factor = fitting_kwargs['psf_iter_factor']
psf_iter_num = fitting_kwargs['psf_iter_num']
compute_bool = fitting_kwargs.get('compute_bands',
[True] * len(self.multi_band_list))
kwargs_psf_iter = fitting_kwargs.get('kwargs_psf_iter', {})
for i in range(len(self.multi_band_list)):
if compute_bool[i] is True:
kwargs_data = self.multi_band_list[i][0]
kwargs_psf = self.multi_band_list[i][1]
kwargs_numerics = self.multi_band_list[i][2]
image_model = class_creator.create_image_model(
kwargs_data=kwargs_data,
kwargs_psf=kwargs_psf,
kwargs_numerics=kwargs_numerics,
kwargs_model=self.kwargs_model)
psf_iter = PsfFitting(image_model_class=image_model,
kwargs_psf_iter=kwargs_psf_iter)
kwargs_psf = psf_iter.update_iterative(kwargs_psf,
lens_updated,
source_updated,
lens_light_input,
ps_input,
factor=psf_iter_factor,
num_iter=psf_iter_num,
verbose=self._verbose,
no_break=True)
self.multi_band_list[i][1] = kwargs_psf
self.fitting.multi_band_list[i][1] = kwargs_psf
return 0
def psf_iteration(self,
num_iter=10,
no_break=True,
stacking_method='median',
block_center_neighbour=0,
keep_psf_error_map=True,
psf_symmetry=1,
psf_iter_factor=1,
error_map_radius=None,
verbose=True,
compute_bands=None):
"""
iterative PSF reconstruction
:param num_iter: number of iterations in the process
:param no_break: bool, if False will break the process as soon as one step lead to a wors reconstruction then the previous step
:param stacking_method: string, 'median' and 'mean' supported
:param block_center_neighbour: radius of neighbouring point source to be blocked in the reconstruction
:param keep_psf_error_map: bool, whether or not to keep the previous psf_error_map
:param psf_symmetry: int, number of invariant rotations in the reconstructed PSF
:param psf_iter_factor: factor of new estimated PSF relative to the old one PSF_updated = (1-psf_iter_factor) * PSF_old + psf_iter_factor*PSF_new
:param error_map_radius: float, radius (in arc seconds) of the outermost error in the PSF estimate
(e.g. to avoid double counting of overlapping PSF errors)
:param verbose: bool, print statements
:param compute_bands: bool list, if multiple bands, this process can be limited to a subset of bands
:return: 0, updated PSF is stored in self.multi_band_list
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
likelihood_mask_list = kwargs_likelihood.get(
'image_likelihood_mask_list', None)
kwargs_pixelbased = kwargs_likelihood.get('kwargs_pixelbased', None)
kwargs_temp = self.best_fit(bijective=False)
if compute_bands is None:
compute_bands = [True] * len(self.multi_band_list)
for band_index in range(len(self.multi_band_list)):
if compute_bands[band_index] is True:
kwargs_psf = self.multi_band_list[band_index][1]
image_model = SingleBandMultiModel(
self.multi_band_list,
kwargs_model,
likelihood_mask_list=likelihood_mask_list,
band_index=band_index,
kwargs_pixelbased=kwargs_pixelbased)
psf_iter = PsfFitting(image_model_class=image_model)
kwargs_psf = psf_iter.update_iterative(
kwargs_psf,
kwargs_params=kwargs_temp,
num_iter=num_iter,
no_break=no_break,
stacking_method=stacking_method,
block_center_neighbour=block_center_neighbour,
keep_psf_error_map=keep_psf_error_map,
psf_symmetry=psf_symmetry,
psf_iter_factor=psf_iter_factor,
error_map_radius=error_map_radius,
verbose=verbose)
self.multi_band_list[band_index][1] = kwargs_psf
return 0
def psf_iteration(self, num_iter=10, no_break=True, stacking_method='median', block_center_neighbour=0, keep_psf_error_map=True,
psf_symmetry=1, psf_iter_factor=1, verbose=True, compute_bands=None):
"""
iterative PSF reconstruction
:param num_iter: number of iterations in the process
:param no_break: bool, if False will break the process as soon as one step lead to a wors reconstruction then the previous step
:param stacking_method: string, 'median' and 'mean' supported
:param block_center_neighbour: radius of neighbouring point source to be blocked in the reconstruction
:param keep_psf_error_map: bool, whether or not to keep the previous psf_error_map
:param psf_symmetry: int, number of invariant rotations in the reconstructed PSF
:param psf_iter_factor: factor of new estimated PSF relative to the old one PSF_updated = (1-psf_iter_factor) * PSF_old + psf_iter_factor*PSF_new
:param verbose: bool, print statements
:param compute_bands: bool list, if multiple bands, this process can be limited to a subset of bands
:return: 0, updated PSF is stored in self.mult_iband_list
"""
kwargs_model = self._updateManager.kwargs_model
kwargs_likelihood = self._updateManager.kwargs_likelihood
likelihood_mask_list = kwargs_likelihood.get('image_likelihood_mask_list', None)
param_class = self._param_class
lens_updated = param_class.update_lens_scaling(self._cosmo_temp, self._lens_temp)
source_updated = param_class.image2source_plane(self._source_temp, lens_updated)
if compute_bands is None:
compute_bands = [True] * len(self.multi_band_list)
for band_index in range(len(self.multi_band_list)):
if compute_bands[band_index] is True:
kwargs_psf = self.multi_band_list[band_index][1]
image_model = SingleBandMultiModel(self.multi_band_list, kwargs_model,
likelihood_mask_list=likelihood_mask_list, band_index=band_index)
psf_iter = PsfFitting(image_model_class=image_model)
kwargs_psf = psf_iter.update_iterative(kwargs_psf, lens_updated, source_updated,
self._lens_light_temp, self._ps_temp, num_iter=num_iter,
no_break=no_break, stacking_method=stacking_method,
block_center_neighbour=block_center_neighbour,
keep_psf_error_map=keep_psf_error_map,
psf_symmetry=psf_symmetry, psf_iter_factor=psf_iter_factor, verbose=verbose)
self.multi_band_list[band_index][1] = kwargs_psf
return 0
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