def test_no_band(self):
"""
test raise statements if band is not evaluated
"""
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.5 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix,
exp_time, sigma_bkg)
kwargs_data['image_data'] = np.ones((numPix, numPix))
kwargs_psf = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'truncation': 5,
'pixel_size': deltaPix
}
kwargs_numerics = {}
multi_band_list = [[kwargs_data, kwargs_psf, kwargs_numerics]]
multi_band = MultiBandImageReconstruction(
multi_band_list, {}, {},
multi_band_type='single-band',
kwargs_likelihood={'bands_compute': [False]})
with self.assertRaises(ValueError):
multi_band.band_setup(band_index=0)
def test_point_source(self):
pointSource = PointSource(point_source_type_list=['SOURCE_POSITION'],
fixed_magnification_list=[True])
kwargs_ps = [{'source_amp': 1000, 'ra_source': 0.1, 'dec_source': 0.1}]
lensModel = LensModel(lens_model_list=['SIS'])
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
numPix = 64
deltaPix = 0.13
kwargs_data = sim_util.data_configure_simple(numPix,
deltaPix,
exposure_time=1,
background_rms=1)
data_class = ImageData(**kwargs_data)
psf_type = "GAUSSIAN"
fwhm = 0.9
kwargs_psf = {'psf_type': psf_type, 'fwhm': fwhm}
psf_class = PSF(**kwargs_psf)
imageModel = ImageModel(data_class=data_class,
psf_class=psf_class,
lens_model_class=lensModel,
point_source_class=pointSource)
image = imageModel.image(kwargs_lens=kwargs_lens, kwargs_ps=kwargs_ps)
assert np.sum(image) > 0
def test_error_map_source(self):
sourceModel = LightModel(light_model_list=['UNIFORM', 'UNIFORM'])
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
exposure_time=1,
background_rms=1)
data_class = ImageData(**kwargs_data)
psf_type = "GAUSSIAN"
fwhm = 0.9
kwargs_psf = {'psf_type': psf_type, 'fwhm': fwhm}
psf_class = PSF(**kwargs_psf)
imageModel = ImageLinearFit(data_class=data_class,
psf_class=psf_class,
lens_model_class=None,
point_source_class=None,
source_model_class=sourceModel)
x_grid, y_grid = util.make_grid(numPix=10, deltapix=1)
error_map = imageModel.error_map_source(kwargs_source=[{
'amp': 1
}, {
'amp': 1
}],
x_grid=x_grid,
y_grid=y_grid,
cov_param=np.array([[1, 0],
[0, 1]]))
assert error_map[0] == 2
def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {'force_minimum_source_surface_brightness': True}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10, deltaPix=0.1, exposure_time=1, sigma_bkg=0.1)
data_class = ImageData(**kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(**kwargs_psf)
kwargs_sersic = {'amp': -1., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class, psf_class, lens_model_class=None, source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {'multi_band_list': [[kwargs_data, kwargs_psf, {}]], 'multi_band_type': 'single-band'}
likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint, kwargs_model=kwargs_model, param_class=param_class, **kwargs_likelihood)
logL, _ = likelihood.logL(args=param_class.kwargs2args(kwargs_source=kwargs_source), verbose=True)
assert logL <= -10**10
def sepc_kwargs_data(self,
supersampling_factor=2,
psf_data=None,
psf_error_map=None):
import lenstronomy.Util.simulation_util as sim_util
kwargs_data = sim_util.data_configure_simple(self.numPix,
self.deltaPix,
inverse=True)
kwargs_data['image_data'] = self.data_process_class.target_stamp
kwargs_data['noise_map'] = self.data_process_class.noise_map
if psf_data is None:
psf_data = self.data_process_class.PSF_list[
self.data_process_class.psf_id_for_fitting]
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf_data,
'pixel_size': self.deltaPix
}
if psf_error_map is not None:
kwargs_psf['psf_error_map'] = psf_error_map
kwargs_numerics = {
'supersampling_factor': supersampling_factor,
'supersampling_convolution': False
}
image_band = [kwargs_data, kwargs_psf, kwargs_numerics]
multi_band_list = [image_band]
self.kwargs_data = kwargs_data
self.kwargs_psf = kwargs_psf
self.kwargs_numerics = kwargs_numerics
self.kwargs_data_joint = {
'multi_band_list': multi_band_list,
'multi_band_type': 'multi-linear'
} # 'single-band', 'multi-linear', 'joint-linear'
def test_extinction_map(self):
kwargs_data = sim_util.data_configure_simple(numPix=10, deltaPix=1, exposure_time=1, background_rms=1)
data_class = ImageData(**kwargs_data)
extinction_class = DifferentialExtinction(optical_depth_model=['UNIFORM'], tau0_index=0)
imageModel = ImageModel(data_class, PSF(), extinction_class=extinction_class)
extinction = imageModel.extinction_map(kwargs_extinction=[{'amp': 1}], kwargs_special={'tau0_list': [1, 0, 0]})
npt.assert_almost_equal(extinction, np.exp(-1))
def test_create_empty(self):
kwargs_data = sim_util.data_configure_simple(numPix=10, deltaPix=1, exposure_time=1, background_rms=1)
data_class = ImageData(**kwargs_data)
imageModel_empty = ImageModel(data_class, PSF())
assert imageModel_empty._psf_error_map == False
flux = imageModel_empty.lens_surface_brightness(kwargs_lens_light=None)
assert flux.all() == 0
def test_update_data(self):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
exposure_time=1,
background_rms=1,
inverse=True)
data_class = ImageData(**kwargs_data)
self.imageModel.update_data(data_class)
assert self.imageModel.Data.num_pixel == 100
def setup(self):
# data specifics
sigma_bkg = .05 # 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 = 0.5 # 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)
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': fwhm, 'truncation': 5, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf)
# 'EXTERNAL_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': 0.0001, 'dec_source': 0.0,
'source_amp': 1.}] # 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': 2, 'supersampling_convolution': True, 'compute_mode': 'gaussian'}
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)
kwargs_data['image_data'] = image_sim
self.solver = LensEquationSolver(lensModel=lens_model_class)
multi_band_list = [[kwargs_data, kwargs_psf, kwargs_numerics]]
kwargs_model = {'lens_model_list': lens_model_list, 'source_light_model_list': source_model_list,
'point_source_model_list': ['SOURCE_POSITION'], 'fixed_magnification_list': [True]}
self.imageModel = MultiLinear(multi_band_list, kwargs_model, likelihood_mask_list=None, compute_bool=None)
def lens_model_plot(ax, lensModel, kwargs_lens, numPix=500, deltaPix=0.01, sourcePos_x=0, sourcePos_y=0,
point_source=False, with_caustics=False, coord_center_ra=0, coord_center_dec=0,
coord_inverse=False):
"""
plots a lens model (convergence) and the critical curves and caustics
:param ax:
:param kwargs_lens:
:param numPix:
:param deltaPix:
:return:
"""
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, center_ra=coord_center_ra, center_dec=coord_center_dec,
inverse=coord_inverse)
data = ImageData(**kwargs_data)
_coords = data
_frame_size = numPix * deltaPix
x_grid, y_grid = data.pixel_coordinates
lensModelExt = LensModelExtensions(lensModel)
#ra_crit_list, dec_crit_list, ra_caustic_list, dec_caustic_list = lensModelExt.critical_curve_caustics(
# kwargs_lens, compute_window=_frame_size, grid_scale=deltaPix/2.)
x_grid1d = util.image2array(x_grid)
y_grid1d = util.image2array(y_grid)
kappa_result = lensModel.kappa(x_grid1d, y_grid1d, kwargs_lens)
kappa_result = util.array2image(kappa_result)
im = ax.matshow(np.log10(kappa_result), origin='lower', extent=[0, _frame_size, 0, _frame_size], cmap='Greys',
vmin=-1, vmax=1) #, cmap=self._cmap, vmin=v_min, vmax=v_max)
if with_caustics is True:
ra_crit_list, dec_crit_list = lensModelExt.critical_curve_tiling(kwargs_lens, compute_window=_frame_size,
start_scale=deltaPix, max_order=10)
ra_caustic_list, dec_caustic_list = lensModel.ray_shooting(ra_crit_list, dec_crit_list, kwargs_lens)
plot_util.plot_line_set(ax, _coords, ra_caustic_list, dec_caustic_list, color='g')
plot_util.plot_line_set(ax, _coords, ra_crit_list, dec_crit_list, color='r')
if point_source:
from lenstronomy.LensModel.Solver.lens_equation_solver import LensEquationSolver
solver = LensEquationSolver(lensModel)
theta_x, theta_y = solver.image_position_from_source(sourcePos_x, sourcePos_y, kwargs_lens,
min_distance=deltaPix, search_window=deltaPix*numPix)
mag_images = lensModel.magnification(theta_x, theta_y, kwargs_lens)
x_image, y_image = _coords.map_coord2pix(theta_x, theta_y)
abc_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
for i in range(len(x_image)):
x_ = (x_image[i] + 0.5) * deltaPix
y_ = (y_image[i] + 0.5) * deltaPix
ax.plot(x_, y_, 'dk', markersize=4*(1 + np.log(np.abs(mag_images[i]))), alpha=0.5)
ax.text(x_, y_, abc_list[i], fontsize=20, color='k')
x_source, y_source = _coords.map_coord2pix(sourcePos_x, sourcePos_y)
ax.plot((x_source + 0.5) * deltaPix, (y_source + 0.5) * deltaPix, '*k', markersize=10)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
return ax
def test_data_configure_simple(self):
# data specifics
sigma_bkg = 1. # background noise per pixel
exp_time = 10 # 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 = 0.5 # 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)
assert data_class.pixel_width == deltaPix
def test_force_positive_source_surface_brightness(self):
kwargs_likelihood = {
'force_positive_source_surface_brightness': True,
'numPix_source': 10,
'deltaPix_source': 0.1
}
kwargs_model = {'source_light_model_list': ['SERSIC']}
kwargs_constraints = {}
param_class = Param(kwargs_model, **kwargs_constraints)
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=0.1,
exposure_time=1,
sigma_bkg=0.1)
data_class = Data(kwargs_data)
kwargs_psf = {'psf_type': 'NONE'}
psf_class = PSF(kwargs_psf)
kwargs_sersic = {
'amp': -1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
source_model_list = ['SERSIC']
kwargs_source = [kwargs_sersic]
source_model_class = LightModel(light_model_list=source_model_list)
imageModel = ImageModel(data_class,
psf_class,
lens_model_class=None,
source_model_class=source_model_class)
image_sim = sim_util.simulate_simple(imageModel, [], kwargs_source)
data_class.update_data(image_sim)
likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=param_class,
**kwargs_likelihood)
logL, _ = likelihood.logL(args=param_class.kwargs2args(
kwargs_source=kwargs_source))
assert logL <= -10**10
def test_point_source_rendering(self):
# initialize data
numPix = 100
deltaPix = 0.05
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exposure_time=1, background_rms=1)
data_class = ImageData(**kwargs_data)
kernel = np.zeros((5, 5))
kernel[2, 2] = 1
kwargs_psf = {'kernel_point_source': kernel, 'psf_type': 'PIXEL', 'psf_error_map': np.ones_like(kernel) * 0.001}
psf_class = PSF(**kwargs_psf)
lens_model_class = LensModel(['SPEP'])
source_model_class = LightModel([])
lens_light_model_class = LightModel([])
kwargs_numerics = {'supersampling_factor': 2, 'supersampling_convolution': True, 'point_source_supersampling_factor': 1}
point_source_class = PointSource(point_source_type_list=['LENSED_POSITION'], fixed_magnification_list=[False])
makeImage = ImageModel(data_class, psf_class, lens_model_class, source_model_class, lens_light_model_class, point_source_class, kwargs_numerics=kwargs_numerics)
# chose point source positions
x_pix = np.array([10, 5, 10, 90])
y_pix = np.array([40, 50, 60, 50])
ra_pos, dec_pos = makeImage.Data.map_pix2coord(x_pix, y_pix)
e1, e2 = param_util.phi_q2_ellipticity(0, 0.8)
kwargs_lens_init = [{'theta_E': 1, 'gamma': 2, 'e1': e1, 'e2': e2, 'center_x': 0, 'center_y': 0}]
kwargs_else = [{'ra_image': ra_pos, 'dec_image': dec_pos, 'point_amp': np.ones_like(ra_pos)}]
image = makeImage.image(kwargs_lens_init, kwargs_source={}, kwargs_lens_light={}, kwargs_ps=kwargs_else)
#print(np.shape(model), 'test')
#image = makeImage.ImageNumerics.array2image(model)
for i in range(len(x_pix)):
npt.assert_almost_equal(image[y_pix[i], x_pix[i]], 1, decimal=2)
x_pix = np.array([10.5, 5.5, 10.5, 90.5])
y_pix = np.array([40, 50, 60, 50])
ra_pos, dec_pos = makeImage.Data.map_pix2coord(x_pix, y_pix)
phi, q = 0., 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_lens_init = [{'theta_E': 1, 'gamma': 2, 'e1': e1, 'e2': e2, 'center_x': 0, 'center_y': 0}]
kwargs_else = [{'ra_image': ra_pos, 'dec_image': dec_pos, 'point_amp': np.ones_like(ra_pos)}]
image = makeImage.image(kwargs_lens_init, kwargs_source={}, kwargs_lens_light={}, kwargs_ps=kwargs_else)
#image = makeImage.ImageNumerics.array2image(model)
for i in range(len(x_pix)):
print(int(y_pix[i]), int(x_pix[i]+0.5))
npt.assert_almost_equal(image[int(y_pix[i]), int(x_pix[i])], 0.5, decimal=1)
npt.assert_almost_equal(image[int(y_pix[i]), int(x_pix[i]+0.5)], 0.5, decimal=1)
def distortions(lensModel,
kwargs_lens,
num_pix=100,
delta_pix=0.05,
center_ra=0,
center_dec=0,
differential_scale=0.0001,
smoothing_scale=None,
**kwargs):
"""
:param lensModel: LensModel instance
:param kwargs_lens: lens model keyword argument list
:param num_pix: number of pixels per axis
:param delta_pix: pixel scale per axis
:param center_ra: center of the grid
:param center_dec: center of the grid
:param differential_scale: scale of the finite derivative length in units of angles
:param smoothing_scale: float or None, Gaussian FWHM of a smoothing kernel applied before plotting
:return: matplotlib instance with different panels
"""
kwargs_grid = sim_util.data_configure_simple(num_pix,
delta_pix,
center_ra=center_ra,
center_dec=center_dec)
_coords = ImageData(**kwargs_grid)
_frame_size = num_pix * delta_pix
ra_grid, dec_grid = _coords.pixel_coordinates
extensions = LensModelExtensions(lensModel=lensModel)
ra_grid1d = util.image2array(ra_grid)
dec_grid1d = util.image2array(dec_grid)
lambda_rad, lambda_tan, orientation_angle, dlambda_tan_dtan, dlambda_tan_drad, dlambda_rad_drad, dlambda_rad_dtan, dphi_tan_dtan, dphi_tan_drad, dphi_rad_drad, dphi_rad_dtan = extensions.radial_tangential_differentials(
ra_grid1d,
dec_grid1d,
kwargs_lens=kwargs_lens,
center_x=center_ra,
center_y=center_dec,
smoothing_3rd=differential_scale,
smoothing_2nd=None)
lambda_rad2d, lambda_tan2d, orientation_angle2d, dlambda_tan_dtan2d, dlambda_tan_drad2d, dlambda_rad_drad2d, dlambda_rad_dtan2d, dphi_tan_dtan2d, dphi_tan_drad2d, dphi_rad_drad2d, dphi_rad_dtan2d = util.array2image(lambda_rad), \
util.array2image(lambda_tan), util.array2image(orientation_angle), util.array2image(dlambda_tan_dtan), util.array2image(dlambda_tan_drad), util.array2image(dlambda_rad_drad), util.array2image(dlambda_rad_dtan), \
util.array2image(dphi_tan_dtan), util.array2image(dphi_tan_drad), util.array2image(dphi_rad_drad), util.array2image(dphi_rad_dtan)
if smoothing_scale is not None:
lambda_rad2d = ndimage.gaussian_filter(lambda_rad2d,
sigma=smoothing_scale /
delta_pix)
dlambda_rad_drad2d = ndimage.gaussian_filter(dlambda_rad_drad2d,
sigma=smoothing_scale /
delta_pix)
lambda_tan2d = np.abs(lambda_tan2d)
# the magnification cut is made to make a stable integral/convolution
lambda_tan2d[lambda_tan2d > 100] = 100
lambda_tan2d = ndimage.gaussian_filter(lambda_tan2d,
sigma=smoothing_scale /
delta_pix)
# the magnification cut is made to make a stable integral/convolution
dlambda_tan_dtan2d[dlambda_tan_dtan2d > 100] = 100
dlambda_tan_dtan2d[dlambda_tan_dtan2d < -100] = -100
dlambda_tan_dtan2d = ndimage.gaussian_filter(dlambda_tan_dtan2d,
sigma=smoothing_scale /
delta_pix)
orientation_angle2d = ndimage.gaussian_filter(orientation_angle2d,
sigma=smoothing_scale /
delta_pix)
dphi_tan_dtan2d = ndimage.gaussian_filter(dphi_tan_dtan2d,
sigma=smoothing_scale /
delta_pix)
def _plot_frame(ax, map, vmin, vmax, text_string):
"""
:param ax: matplotlib.axis instance
:param map: 2d array
:param vmin: minimum plotting scale
:param vmax: maximum plotting scale
:param text_string: string to describe the label
:return:
"""
font_size = 10
_arrow_size = 0.02
im = ax.matshow(map,
extent=[0, _frame_size, 0, _frame_size],
vmin=vmin,
vmax=vmax)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = plt.colorbar(im, cax=cax, orientation='vertical')
#cb.set_label(text_string, fontsize=10)
#plot_util.scale_bar(ax, _frame_size, dist=1, text='1"', font_size=font_size)
plot_util.text_description(ax,
_frame_size,
text=text_string,
color="k",
backgroundcolor='w',
font_size=font_size)
#if 'no_arrow' not in kwargs or not kwargs['no_arrow']:
# plot_util.coordinate_arrows(ax, _frame_size, _coords,
# color='w', arrow_size=_arrow_size,
# font_size=font_size)
f, axes = plt.subplots(3, 4, figsize=(12, 8))
_plot_frame(axes[0, 0],
lambda_rad2d,
vmin=0.6,
vmax=1.4,
text_string=r"$\lambda_{rad}$")
_plot_frame(axes[0, 1],
lambda_tan2d,
vmin=-20,
vmax=20,
text_string=r"$\lambda_{tan}$")
_plot_frame(axes[0, 2],
orientation_angle2d,
vmin=-np.pi / 10,
vmax=np.pi / 10,
text_string=r"$\phi$")
_plot_frame(axes[0, 3],
util.array2image(lambda_tan * lambda_rad),
vmin=-20,
vmax=20,
text_string='magnification')
_plot_frame(axes[1, 0],
dlambda_rad_drad2d / lambda_rad2d,
vmin=-.1,
vmax=.1,
text_string='dlambda_rad_drad')
_plot_frame(axes[1, 1],
dlambda_tan_dtan2d / lambda_tan2d,
vmin=-20,
vmax=20,
text_string='dlambda_tan_dtan')
_plot_frame(axes[1, 2],
dlambda_tan_drad2d / lambda_tan2d,
vmin=-20,
vmax=20,
text_string='dlambda_tan_drad')
_plot_frame(axes[1, 3],
dlambda_rad_dtan2d / lambda_rad2d,
vmin=-.1,
vmax=.1,
text_string='dlambda_rad_dtan')
_plot_frame(axes[2, 0],
dphi_rad_drad2d,
vmin=-.1,
vmax=.1,
text_string='dphi_rad_drad')
_plot_frame(axes[2, 1],
dphi_tan_dtan2d,
vmin=0,
vmax=20,
text_string='dphi_tan_dtan: curvature radius')
_plot_frame(axes[2, 2],
dphi_tan_drad2d,
vmin=-.1,
vmax=.1,
text_string='dphi_tan_drad')
_plot_frame(axes[2, 3],
dphi_rad_dtan2d,
vmin=0,
vmax=20,
text_string='dphi_rad_dtan')
return f, axes
def arrival_time_surface(ax,
lensModel,
kwargs_lens,
numPix=500,
deltaPix=0.01,
sourcePos_x=0,
sourcePos_y=0,
with_caustics=False,
point_source=False,
n_levels=10,
kwargs_contours={},
image_color_list=None,
letter_font_size=20):
"""
:param ax:
:param lensModel:
:param kwargs_lens:
:param numPix:
:param deltaPix:
:param sourcePos_x:
:param sourcePos_y:
:param with_caustics:
:return:
"""
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix)
data = ImageData(**kwargs_data)
_frame_size = numPix * deltaPix
_coords = data
x_grid, y_grid = data.pixel_coordinates
lensModelExt = LensModelExtensions(lensModel)
#ra_crit_list, dec_crit_list, ra_caustic_list, dec_caustic_list = lensModelExt.critical_curve_caustics(
# kwargs_lens, compute_window=_frame_size, grid_scale=deltaPix/2.)
x_grid1d = util.image2array(x_grid)
y_grid1d = util.image2array(y_grid)
fermat_surface = lensModel.fermat_potential(x_grid1d, y_grid1d,
kwargs_lens, sourcePos_x,
sourcePos_y)
fermat_surface = util.array2image(fermat_surface)
#, cmap='Greys', vmin=-1, vmax=1) #, cmap=self._cmap, vmin=v_min, vmax=v_max)
if with_caustics is True:
ra_crit_list, dec_crit_list = lensModelExt.critical_curve_tiling(
kwargs_lens,
compute_window=_frame_size,
start_scale=deltaPix / 5,
max_order=10)
ra_caustic_list, dec_caustic_list = lensModel.ray_shooting(
ra_crit_list, dec_crit_list, kwargs_lens)
plot_util.plot_line_set(ax,
_coords,
ra_caustic_list,
dec_caustic_list,
shift=_frame_size / 2.,
color='g')
plot_util.plot_line_set(ax,
_coords,
ra_crit_list,
dec_crit_list,
shift=_frame_size / 2.,
color='r')
if point_source is True:
from lenstronomy.LensModel.Solver.lens_equation_solver import LensEquationSolver
solver = LensEquationSolver(lensModel)
theta_x, theta_y = solver.image_position_from_source(
sourcePos_x,
sourcePos_y,
kwargs_lens,
min_distance=deltaPix,
search_window=deltaPix * numPix)
fermat_pot_images = lensModel.fermat_potential(theta_x, theta_y,
kwargs_lens)
im = ax.contour(
x_grid,
y_grid,
fermat_surface,
origin='lower', # extent=[0, _frame_size, 0, _frame_size],
levels=np.sort(fermat_pot_images),
**kwargs_contours)
mag_images = lensModel.magnification(theta_x, theta_y, kwargs_lens)
x_image, y_image = _coords.map_coord2pix(theta_x, theta_y)
abc_list = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
for i in range(len(x_image)):
x_ = (x_image[i] + 0.5) * deltaPix - _frame_size / 2
y_ = (y_image[i] + 0.5) * deltaPix - _frame_size / 2
if image_color_list is None:
color = 'k'
else:
color = image_color_list[i]
ax.plot(x_, y_, 'x', markersize=10, alpha=1, color=color
) # markersize=8*(1 + np.log(np.abs(mag_images[i])))
ax.text(x_ + deltaPix,
y_ + deltaPix,
abc_list[i],
fontsize=letter_font_size,
color='k')
x_source, y_source = _coords.map_coord2pix(sourcePos_x, sourcePos_y)
ax.plot((x_source + 0.5) * deltaPix - _frame_size / 2,
(y_source + 0.5) * deltaPix - _frame_size / 2,
'*k',
markersize=20)
else:
vmin = np.min(fermat_surface)
vmax = np.max(fermat_surface)
levels = np.linspace(start=vmin, stop=vmax, num=n_levels)
im = ax.contour(
x_grid,
y_grid,
fermat_surface,
origin='lower', # extent=[0, _frame_size, 0, _frame_size],
levels=levels,
**kwargs_contours)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
return ax
def setup(self):
np.random.seed(42)
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
kwargs_model = {'lens_model_list': ['SPEP'],
'lens_light_model_list': ['SERSIC'],
'source_light_model_list': ['SERSIC'],
'point_source_model_list': ['SOURCE_POSITION'],
'fixed_magnification_list': [True]}
# PSF specification
kwargs_band = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
data_class = ImageData(**kwargs_band)
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': fwhm, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf)
print(np.shape(psf_class.kernel_point_source), 'test kernel shape -')
kwargs_spep = {'theta_E': 1., 'gamma': 1.95, 'center_x': 0, 'center_y': 0, 'e1': 0.1, 'e2': 0.1}
self.kwargs_lens = [kwargs_spep]
kwargs_sersic = {'amp': 1/0.05**2., 'R_sersic': 0.1, 'n_sersic': 2, 'center_x': 0, 'center_y': 0}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {'amp': 1., 'R_sersic': .6, 'n_sersic': 3, 'center_x': 0, 'center_y': 0}
self.kwargs_lens_light = [kwargs_sersic]
self.kwargs_source = [kwargs_sersic_ellipse]
self.kwargs_ps = [{'ra_source': 0.55, 'dec_source': 0.02,
'source_amp': 1.}] # quasar point source position in the source plane and intrinsic brightness
self.kwargs_cosmo = {'D_dt': 1000}
kwargs_numerics = {'supersampling_factor': 1, 'supersampling_convolution': False}
lens_model_class, source_model_class, lens_light_model_class, point_source_class, extinction_class = class_creator.create_class_instances(**kwargs_model)
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
lens_light_model_class, point_source_class, extinction_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)
ra_pos, dec_pos = imageModel.PointSource.image_position(kwargs_ps=self.kwargs_ps, kwargs_lens=self.kwargs_lens)
data_class.update_data(image_sim)
kwargs_band['image_data'] = image_sim
self.data_class = data_class
self.psf_class = psf_class
self.kwargs_model = kwargs_model
self.kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False}
kwargs_constraints = {
'num_point_source_list': [4],
'solver_type': 'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
'Ddt_sampling': True
}
def condition_definition(kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps=None, kwargs_special=None, kwargs_extinction=None):
logL = 0
if kwargs_lens_light[0]['R_sersic'] > kwargs_source[0]['R_sersic']:
logL -= 10**15
return logL
kwargs_likelihood = {'force_no_add_image': True,
'source_marg': True,
'astrometric_likelihood': True,
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
'flux_ratio_likelihood': True,
'prior_lens': [[0, 'theta_E', 1, 0.1]],
'custom_logL_addition': condition_definition,
'image_position_likelihood': True
}
self.kwargs_data = {'multi_band_list': [[kwargs_band, kwargs_psf, kwargs_numerics]], 'multi_band_type': 'single-band',
'time_delays_measured': np.ones(4),
'time_delays_uncertainties': np.ones(4),
'flux_ratios': np.ones(4),
'flux_ratio_errors': np.ones(4),
'ra_image_list': ra_pos,
'dec_image_list': dec_pos
}
self.param_class = Param(self.kwargs_model, **kwargs_constraints)
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.Likelihood = LikelihoodModule(kwargs_data_joint=self.kwargs_data, kwargs_model=kwargs_model, param_class=self.param_class, **kwargs_likelihood)
self.kwargs_band = kwargs_band
self.kwargs_psf = kwargs_psf
self.numPix = numPix
def setup(self):
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.1 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # 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)
kwargs_psf_gaussian = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix
}
psf = PSF(**kwargs_psf_gaussian)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf.kernel_point_source
}
psf_class = PSF(**kwargs_psf)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP']
self.kwargs_lens = [kwargs_spemd]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
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)
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False,
'compute_mode': 'regular'
}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light)
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
kwargs_data_joint = {
'multi_band_list': [[kwargs_data, kwargs_psf, kwargs_numerics]],
'multi_band_type': 'single-band'
}
self.data_class = data_class
self.psf_class = psf_class
kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'fixed_magnification_list': [False],
}
self.kwargs_numerics = {'subgrid_res': 1, 'psf_subgrid': False}
kwargs_constraints = {
'image_plane_source_list': [False] * len(source_model_list)
}
kwargs_likelihood = {
'source_marg': False,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
self.param_class = Param(kwargs_model, **kwargs_constraints)
self.Likelihood = LikelihoodModule(kwargs_data_joint=kwargs_data_joint,
kwargs_model=kwargs_model,
param_class=self.param_class,
**kwargs_likelihood)
prior_means = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
prior_sigmas = np.ones_like(prior_means) * 0.1
self.output_dir = 'test_nested_out'
self.sampler = MultiNestSampler(self.Likelihood,
prior_type='uniform',
prior_means=prior_means,
prior_sigmas=prior_sigmas,
output_dir=self.output_dir,
remove_output_dir=True)
def setup(self):
# data specifics
sigma_bkg = .05 # 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 = 0.5 # 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)
kwargs_psf = sim_util.psf_configure_simple(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=31,
deltaPix=deltaPix,
truncate=5)
psf_class = PSF(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', 'SERSIC']
self.kwargs_lens_light = [kwargs_sersic, kwargs_sersic]
lens_light_model_class = LightModel(light_model_list=['SERSIC'])
source_model_list = ['SERSIC_ELLIPSE', 'SERSIC_ELLIPSE']
self.kwargs_source = [kwargs_sersic_ellipse, kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=['SERSIC_ELLIPSE'])
self.kwargs_ps = [
{
'ra_source': 0.0001,
'dec_source': 0.0,
'source_amp': 1.
}
] # 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': 2, '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,
[kwargs_sersic_ellipse],
[kwargs_sersic], self.kwargs_ps)
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
self.solver = LensEquationSolver(lensModel=lens_model_class)
kwargs_model_bool = {
'index_source_light_model': [0],
'index_lens_light_model': [0]
}
multi_band_list = [[
kwargs_data, kwargs_psf, kwargs_numerics, kwargs_model_bool
]]
self.imageModel = MultiBandMultiModel(multi_band_list,
lens_model_class,
source_model_list,
lens_light_model_list,
point_source_class)
# vgrad = np.gradient(lens_data)
# fulgrad = np.sqrt(vgrad[0]**2 + vgrad[1]**2)
# len_std = (abs(lens_data/exp_time)+stdd**2)**0.5
# len_std = len_std + fulgrad/fulgrad.max() * len_std.max()
deltaPix = 0.08
psf = pyfits.getdata(folder + 'Drz_PSF.fits')
psf_fsize = 77
psf_half_r = int(psf_fsize / 2)
psf_peak = np.where(psf == psf.max())
psf_peak = [psf_peak[0][0], psf_peak[1][0]]
psf = psf[psf_peak[0] - psf_half_r:psf_peak[0] + psf_half_r + 1,
psf_peak[1] - psf_half_r:psf_peak[1] + psf_half_r + 1]
kwargs_data = sim_util.data_configure_simple(
numPix=framesize, deltaPix=deltaPix) #,inverse=True)
kwargs_data['image_data'] = lens_data
kwargs_data['noise_map'] = len_std
data_class = ImageData(**kwargs_data)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf,
'pixel_size': deltaPix
}
psf_class = PSF(**kwargs_psf)
#%%
# lens model choicers
fixed_lens = []
kwargs_lens_init = []
numPix = int( 240 * 0.13/0.05 ) + 1 # pixel size #!!!
deltaPix = 0.05/4 # pixel size in arcsec (area per pixel = deltaPix**2)
psf = pyfits.open('psf_{0}_sub4.fits'.format(filt))
psf_data = psf[0].data
cut =25
# psf_data = psf_data[1+cut:-cut,1+cut:-cut]+ 0 #shave PSF to singular size, as max in the middle
plt.imshow(psf_data, origin='lower',cmap='gist_heat', norm=LogNorm())
plt.colorbar()
plt.close()
kwargs_psf_high_res = {'psf_type': 'PIXEL', 'kernel_point_source': psf_data, 'pixel_size': deltaPix}
psf_class = PSF(**kwargs_psf_high_res)
zp= 25.9463 #Assuming it same as F160W
kwargs_data_high_res = sim_util.data_configure_simple(numPix, deltaPix) #,inverse=True)
data_class = Data(**kwargs_data_high_res)
kwargs_numerics = {'supersampling_factor': 12, 'supersampling_convolution': False}
import sys
sys.path.insert(0,'../../share_tools/')
from gene_para import gene_para
for seed in range(702, 799):
print(seed)
para=gene_para(seed=seed,fixh0=102)
#==============================================================================
# #######lens light
#==============================================================================
from lenstronomy.LightModel.light_model import LightModel
lens_light_para=para.lens_light()
np.random.seed(seed)
def __init__(self, *args, **kwargs):
super(TestRaise, self).__init__(*args, **kwargs)
# data specifics
sigma_bkg = .05 # 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 = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix,
deltaPix,
exp_time,
sigma_bkg,
inverse=True)
data_class = ImageData(**kwargs_data)
kwargs_psf = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'truncation': 5,
'pixel_size': deltaPix
}
psf_class = PSF(**kwargs_psf)
kernel = psf_class.kernel_point_source
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': kernel,
'psf_error_map': np.ones_like(kernel) * 0.001
}
psf_class = PSF(**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']
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']
kwargs_lens_light = [kwargs_sersic]
lens_light_model_class = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
kwargs_ps = [
{
'ra_source': 0.01,
'dec_source': 0.0,
'source_amp': 1.
}
] # 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': 2}
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, kwargs_lens,
kwargs_source, kwargs_lens_light,
kwargs_ps)
data_class.update_data(image_sim)
self.imageModel = imageModel
self.kwargs_sparse_solver = {}
def make_lensmodel(lens_info, theta_E, source_info, box_f):
# lens data specifics
lens_image = lens_info['image']
psf_lens = lens_info['psf']
background_rms = background_rms_image(5, lens_image)
exposure_time = 100
kwargs_data_lens = sim_util.data_configure_simple(len(lens_image),
lens_info['deltapix'],
exposure_time,
background_rms)
kwargs_data_lens['image_data'] = lens_image
data_class_lens = ImageData(**kwargs_data_lens)
#PSF
kwargs_psf_lens = {
'psf_type': 'PIXEL',
'pixel_size': lens_info['deltapix'],
'kernel_point_source': psf_lens
}
psf_class_lens = PSF(**kwargs_psf_lens)
# lens light model
lens_light_model_list = ['SERSIC_ELLIPSE']
lens_light_model_class = LightModel(light_model_list=lens_light_model_list)
kwargs_model = {'lens_light_model_list': lens_light_model_list}
kwargs_numerics_galfit = {'supersampling_factor': 1}
kwargs_constraints = {}
kwargs_likelihood = {'check_bounds': True}
image_band = [kwargs_data_lens, kwargs_psf_lens, kwargs_numerics_galfit]
multi_band_list = [image_band]
kwargs_data_joint = {
'multi_band_list': multi_band_list,
'multi_band_type': 'multi-linear'
}
# Sersic component
fixed_lens_light = [{}]
kwargs_lens_light_init = [{
'R_sersic': .1,
'n_sersic': 4,
'e1': 0,
'e2': 0,
'center_x': 0,
'center_y': 0
}]
kwargs_lens_light_sigma = [{
'n_sersic': 0.5,
'R_sersic': 0.2,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
}]
kwargs_lower_lens_light = [{
'e1': -0.5,
'e2': -0.5,
'R_sersic': 0.01,
'n_sersic': 0.5,
'center_x': -10,
'center_y': -10
}]
kwargs_upper_lens_light = [{
'e1': 0.5,
'e2': 0.5,
'R_sersic': 10,
'n_sersic': 8,
'center_x': 10,
'center_y': 10
}]
lens_light_params = [
kwargs_lens_light_init, kwargs_lens_light_sigma, fixed_lens_light,
kwargs_lower_lens_light, kwargs_upper_lens_light
]
kwargs_params = {'lens_light_model': lens_light_params}
fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model,
kwargs_constraints, kwargs_likelihood,
kwargs_params)
fitting_kwargs_list = [[
'PSO', {
'sigma_scale': 1.,
'n_particles': 50,
'n_iterations': 50
}
]]
chain_list = fitting_seq.fit_sequence(fitting_kwargs_list)
kwargs_result = fitting_seq.best_fit()
modelPlot = ModelPlot(multi_band_list, kwargs_model, kwargs_result)
# Lens light best result
kwargs_light_lens = kwargs_result['kwargs_lens_light'][0]
#Lens model
kwargs_lens_list = [{
'theta_E': theta_E,
'e1': kwargs_light_lens['e1'],
'e2': kwargs_light_lens['e2'],
'center_x': kwargs_light_lens['center_x'],
'center_y': kwargs_light_lens['center_y']
}]
lensModel = LensModel(['SIE'])
lme = LensModelExtensions(lensModel)
#random position for the source
x_crit_list, y_crit_list = lme.critical_curve_tiling(
kwargs_lens_list,
compute_window=(len(source_info['image'])) * (source_info['deltapix']),
start_scale=source_info['deltapix'],
max_order=10)
if len(x_crit_list) > 2 and len(y_crit_list) > 2:
x_caustic_list, y_caustic_list = lensModel.ray_shooting(
x_crit_list, y_crit_list, kwargs_lens_list)
xsamp0 = np.arange(
min(x_caustic_list) - min(x_caustic_list) * box_f[0],
max(x_caustic_list) + max(x_caustic_list) * box_f[1], 0.1)
xsamp = xsamp0[abs(xsamp0.round(1)) != 0.1]
ysamp0 = np.arange(
min(y_caustic_list) - min(y_caustic_list) * box_f[0],
max(y_caustic_list) + max(y_caustic_list) * box_f[1], 0.1)
ysamp = ysamp0[abs(ysamp0.round(1)) != 0.1]
if len(xsamp) == 0 or len(ysamp) == 0:
x_shift, y_shift = 0.15, 0.15 #arcseconds
else:
y_shift = rand.sample(list(ysamp), 1)[0]
x_shift = rand.sample(list(xsamp), 1)[0]
else:
x_shift, y_shift = -0.15, 0.15 #arcseconds
x_caustic_list = [0]
y_caustic_list = [0]
solver = LensEquationSolver(lensModel)
theta_ra, theta_dec = solver.image_position_from_source(
x_shift, y_shift, kwargs_lens_list)
if len(theta_ra) <= 1:
x_shift, y_shift = -0.2, -0.2 #arcseconds1
if abs(x_shift) >= int(theta_E) or abs(y_shift) >= int(theta_E):
x_shift, y_shift = 0.3, -0.3
print('BLABLA')
print('HERE',
min(x_caustic_list) - min(x_caustic_list) * box_f[0],
max(x_caustic_list) + max(x_caustic_list) * box_f[1],
min(y_caustic_list) - min(y_caustic_list) * box_f[0],
max(y_caustic_list) + max(y_caustic_list) * box_f[1])
return {
'lens_light_model_list': ['SERSIC_ELLIPSE'],
'kwargs_light_lens': [kwargs_light_lens],
'lens_light_model_class': lens_light_model_class,
'kwargs_lens_list': kwargs_lens_list,
'kwargs_data_lens': kwargs_data_lens,
'source_shift': [x_shift, y_shift]
}
def __init__(self, *args, **kwargs):
super(TestRaise, self).__init__(*args, **kwargs)
# data specifics
sigma_bkg = .05 # 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 = 0.5 # full width half max of PSF
# PSF specification
kwargs_data = sim_util.data_configure_simple(numPix,
deltaPix,
exp_time,
sigma_bkg,
inverse=True)
self.data_class = ImageData(**kwargs_data)
kwargs_psf = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'truncation': 5,
'pixel_size': deltaPix
}
psf_class = PSF(**kwargs_psf)
kernel = psf_class.kernel_point_source
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': kernel,
'psf_error_map': np.ones_like(kernel) * 0.001
}
self.psf_class = PSF(**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]
self.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']
kwargs_lens_light_base = [kwargs_sersic]
lens_light_model_class_base = LightModel(
light_model_list=lens_light_model_list)
source_model_list = ['SERSIC_ELLIPSE']
kwargs_source_base = [kwargs_sersic_ellipse]
source_model_class_base = LightModel(
light_model_list=source_model_list)
self.kwargs_ps = [
{
'ra_source': 0.01,
'dec_source': 0.0,
'source_amp': 1.
}
] # quasar point source position in the source plane and intrinsic brightness
point_source_class_base = PointSource(
point_source_type_list=['SOURCE_POSITION'],
fixed_magnification_list=[True])
kwargs_numerics_base = {
'supersampling_factor': 2,
'supersampling_convolution': False
}
imageModel_base = ImageModel(self.data_class,
self.psf_class,
self.lens_model_class,
source_model_class_base,
lens_light_model_class_base,
point_source_class_base,
kwargs_numerics=kwargs_numerics_base)
image_sim = sim_util.simulate_simple(imageModel_base, self.kwargs_lens,
kwargs_source_base,
kwargs_lens_light_base,
self.kwargs_ps)
self.data_class.update_data(image_sim)
# create a starlet light distributions
n_scales = 6
source_map = imageModel_base.source_surface_brightness(
kwargs_source_base, de_lensed=True, unconvolved=True)
starlets_class = SLIT_Starlets(force_no_pysap=_force_no_pysap)
source_map_starlets = starlets_class.decomposition_2d(
source_map, n_scales)
self.kwargs_source = [{
'amp': source_map_starlets,
'n_scales': n_scales,
'n_pixels': numPix,
'scale': deltaPix,
'center_x': 0,
'center_y': 0
}]
self.source_model_class = LightModel(
light_model_list=['SLIT_STARLETS'])
lens_light_map = imageModel_base.lens_surface_brightness(
kwargs_lens_light_base, unconvolved=True)
starlets_class = SLIT_Starlets(force_no_pysap=_force_no_pysap,
second_gen=True)
lens_light_starlets = starlets_class.decomposition_2d(
lens_light_map, n_scales)
self.kwargs_lens_light = [{
'amp': lens_light_starlets,
'n_scales': n_scales,
'n_pixels': numPix,
'scale': deltaPix,
'center_x': 0,
'center_y': 0
}]
self.lens_light_model_class = LightModel(
light_model_list=['SLIT_STARLETS_GEN2'])
self.kwargs_numerics = {'supersampling_factor': 1}
self.kwargs_pixelbased = {
'supersampling_factor_source':
2, # supersampling of pixelated source grid
# following choices are to minimize pixel solver runtime (not to get accurate reconstruction!)
'threshold_decrease_type': 'none',
'num_iter_source': 2,
'num_iter_lens': 2,
'num_iter_global': 2,
'num_iter_weights': 2,
}
def setup(self):
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.05 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # full width half max of PSF
# PSF specification
self.kwargs_data = sim_util.data_configure_simple(
numPix, deltaPix, exp_time, sigma_bkg)
data_class = ImageData(**self.kwargs_data)
kwargs_psf_gaussian = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix,
'truncation': 3
}
psf_gaussian = PSF(**kwargs_psf_gaussian)
self.kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf_gaussian.kernel_point_source,
'psf_error_map': np.zeros_like(psf_gaussian.kernel_point_source)
}
psf_class = PSF(**self.kwargs_psf)
# 'EXTERNAL_SHEAR': external shear
kwargs_shear = {
'gamma1': 0.01,
'gamma2': 0.01
} # gamma_ext: shear strength, psi_ext: shear angel (in radian)
kwargs_spemd = {
'theta_E': 1.,
'gamma': 1.8,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
lens_model_list = ['SPEP', 'SHEAR']
self.kwargs_lens = [kwargs_spemd, kwargs_shear]
lens_model_class = LensModel(lens_model_list=lens_model_list)
kwargs_sersic = {
'amp': 1.,
'R_sersic': 0.1,
'n_sersic': 2,
'center_x': 0,
'center_y': 0
}
# 'SERSIC_ELLIPSE': elliptical Sersic profile
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': .6,
'n_sersic': 3,
'center_x': 0,
'center_y': 0,
'e1': 0.1,
'e2': 0.1
}
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': 1.
}
] # quasar point source position in the source plane and intrinsic brightness
point_source_list = ['SOURCE_POSITION']
point_source_class = PointSource(
point_source_type_list=point_source_list,
fixed_magnification_list=[True])
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False,
'compute_mode': 'regular',
'point_source_supersampling_factor': 1
}
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.data_class = data_class
self.psf_class = psf_class
self.kwargs_data['image_data'] = image_sim
self.kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
'lens_light_model_list': lens_light_model_list,
'point_source_model_list': point_source_list,
'fixed_magnification_list': [False],
'index_lens_model_list': [[0, 1]],
}
self.kwargs_numerics = kwargs_numerics
num_source_model = len(source_model_list)
self.kwargs_constraints = {
'num_point_source_list': [4],
'image_plane_source_list': [False] * num_source_model,
'solver_type':
'NONE', # 'PROFILE', 'PROFILE_SHEAR', 'ELLIPSE', 'CENTER'
}
self.kwargs_likelihood = {
'force_no_add_image': True,
'source_marg': True,
'linear_prior': [1],
'image_position_uncertainty': 0.004,
'check_matched_source_position': False,
'source_position_tolerance': 0.001,
'source_position_sigma': 0.001,
'check_positive_flux': True,
}
lens_sigma = [{
'theta_E': 0.1,
'gamma': 0.1,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
}, {
'gamma1': 0.1,
'gamma2': 0.1
}]
lens_lower = [{
'theta_E': 0.,
'gamma': 1.5,
'center_x': -2,
'center_y': -2,
'e1': -0.4,
'e2': -0.4
}, {
'gamma1': -0.3,
'gamma2': -0.3
}]
lens_upper = [{
'theta_E': 10.,
'gamma': 2.5,
'center_x': 2,
'center_y': 2,
'e1': 0.4,
'e2': 0.4
}, {
'gamma1': 0.3,
'gamma2': 0.3
}]
source_sigma = [{
'R_sersic': 0.05,
'n_sersic': 0.5,
'center_x': 0.1,
'center_y': 0.1,
'e1': 0.1,
'e2': 0.1
}]
source_lower = [{
'R_sersic': 0.01,
'n_sersic': 0.5,
'center_x': -2,
'center_y': -2,
'e1': -0.4,
'e2': -0.4
}]
source_upper = [{
'R_sersic': 10,
'n_sersic': 5.5,
'center_x': 2,
'center_y': 2,
'e1': 0.4,
'e2': 0.4
}]
lens_light_sigma = [{
'R_sersic': 0.05,
'n_sersic': 0.5,
'center_x': 0.1,
'center_y': 0.1
}]
lens_light_lower = [{
'R_sersic': 0.01,
'n_sersic': 0.5,
'center_x': -2,
'center_y': -2
}]
lens_light_upper = [{
'R_sersic': 10,
'n_sersic': 5.5,
'center_x': 2,
'center_y': 2
}]
ps_sigma = [{'ra_source': 1, 'dec_source': 1, 'point_amp': 1}]
lens_param = self.kwargs_lens, lens_sigma, [{}, {
'ra_0': 0,
'dec_0': 0
}], lens_lower, lens_upper
source_param = self.kwargs_source, source_sigma, [
{}
], source_lower, source_upper
lens_light_param = self.kwargs_lens_light, lens_light_sigma, [{
'center_x':
0
}], lens_light_lower, lens_light_upper
ps_param = self.kwargs_ps, ps_sigma, [{}
], self.kwargs_ps, self.kwargs_ps
self.kwargs_params = {
'lens_model': lens_param,
'source_model': source_param,
'lens_light_model': lens_light_param,
'point_source_model': ps_param,
# 'cosmography': cosmo_param
}
image_band = [self.kwargs_data, self.kwargs_psf, self.kwargs_numerics]
multi_band_list = [image_band]
self.kwargs_data_joint = {
'multi_band_list': multi_band_list,
'multi_band_type': 'multi-linear'
}
import lenstronomy.Util.simulation_util as sim_util
import lenstronomy.Util.image_util as image_util
from lenstronomy.Data.imaging_data import ImageData
from lenstronomy.Data.psf import PSF
# data specifics
background_rms = .05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 50 # cutout pixel size
deltaPix = 0.3 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.8 # full width half max of PSF
kwargs_data = sim_util.data_configure_simple(numPix,
deltaPix,
exp_time,
background_rms,
inverse=True)
data_class = ImageData(**kwargs_data)
# PSF specification
kwargs_psf = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix,
'truncation': 6
}
psf_class = PSF(**kwargs_psf)
# create a model with three Sersic profiles
# all the models are part of 'lens_light_model_list', meaning that their surface brightness profile are not lensed
lens_light_model_list = ['SERSIC_ELLIPSE']
from lenstronomy.LightModel.light_model import LightModel
def test_zeus(self):
# we make a very basic lens+source model to feed to check zeus can be run through fitting sequence
# we don't use the kwargs defined in setup() as those are modified during the tests; using unique kwargs here is safer
# data specifics
sigma_bkg = 0.05 # background noise per pixel
exp_time = 100 # exposure time (arbitrary units, flux per pixel is in units #photons/exp_time unit)
numPix = 10 # cutout pixel size
deltaPix = 0.05 # pixel size in arcsec (area per pixel = deltaPix**2)
fwhm = 0.5 # 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)
kwargs_psf_gaussian = {
'psf_type': 'GAUSSIAN',
'fwhm': fwhm,
'pixel_size': deltaPix,
'truncation': 3
}
psf_gaussian = PSF(**kwargs_psf_gaussian)
kwargs_psf = {
'psf_type': 'PIXEL',
'kernel_point_source': psf_gaussian.kernel_point_source,
'psf_error_map': np.zeros_like(psf_gaussian.kernel_point_source)
}
psf_class = PSF(**kwargs_psf)
# make a lens
lens_model_list = ['EPL']
kwargs_epl = {
'theta_E': 0.6,
'gamma': 2.6,
'center_x': 0.0,
'center_y': 0.0,
'e1': 0.1,
'e2': 0.1
}
kwargs_lens = [kwargs_epl]
lens_model_class = LensModel(lens_model_list=lens_model_list)
# make a source
source_model_list = ['SERSIC_ELLIPSE']
kwargs_sersic_ellipse = {
'amp': 1.,
'R_sersic': 0.6,
'n_sersic': 3,
'center_x': 0.0,
'center_y': 0.0,
'e1': 0.1,
'e2': 0.1
}
kwargs_source = [kwargs_sersic_ellipse]
source_model_class = LightModel(light_model_list=source_model_list)
kwargs_numerics = {
'supersampling_factor': 1,
'supersampling_convolution': False
}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = sim_util.simulate_simple(imageModel, kwargs_lens,
kwargs_source)
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
kwargs_model = {
'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list
}
lens_fixed = [{}]
lens_sigma = [{
'theta_E': 0.1,
'gamma': 0.1,
'e1': 0.1,
'e2': 0.1,
'center_x': 0.1,
'center_y': 0.1
}]
lens_lower = [{
'theta_E': 0.,
'gamma': 1.5,
'center_x': -2,
'center_y': -2,
'e1': -0.4,
'e2': -0.4
}]
lens_upper = [{
'theta_E': 10.,
'gamma': 2.5,
'center_x': 2,
'center_y': 2,
'e1': 0.4,
'e2': 0.4
}]
source_fixed = [{}]
source_sigma = [{
'R_sersic': 0.05,
'n_sersic': 0.5,
'center_x': 0.1,
'center_y': 0.1,
'e1': 0.1,
'e2': 0.1
}]
source_lower = [{
'R_sersic': 0.01,
'n_sersic': 0.5,
'center_x': -2,
'center_y': -2,
'e1': -0.4,
'e2': -0.4
}]
source_upper = [{
'R_sersic': 10,
'n_sersic': 5.5,
'center_x': 2,
'center_y': 2,
'e1': 0.4,
'e2': 0.4
}]
lens_param = [
kwargs_lens, lens_sigma, lens_fixed, lens_lower, lens_upper
]
source_param = [
kwargs_source, source_sigma, source_fixed, source_lower,
source_upper
]
kwargs_params = {
'lens_model': lens_param,
'source_model': source_param
}
kwargs_constraints = {}
multi_band_list = [[kwargs_data, kwargs_psf, kwargs_numerics]]
kwargs_data_joint = {
'multi_band_list': multi_band_list,
'multi_band_type': 'multi-linear'
}
kwargs_likelihood = {'source_marg': True}
fittingSequence = FittingSequence(kwargs_data_joint, kwargs_model,
kwargs_constraints,
kwargs_likelihood, kwargs_params)
fitting_list = []
kwargs_zeus = {
'sampler_type': 'ZEUS',
'n_burn': 2,
'n_run': 2,
'walkerRatio': 4
}
fitting_list.append(['MCMC', kwargs_zeus])
chain_list = fittingSequence.fit_sequence(fitting_list)
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_raise(self):
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
background_rms=1)
#kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(
multi_band_list=[[kwargs_data, {
'psf_type': 'NONE'
}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
arrow_size=0.02,
cmap_string="gist_heat")
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
background_rms=1)
# kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(multi_band_list=[[kwargs_data, {}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
arrow_size=0.02,
cmap_string="gist_heat")
f, ax = plt.subplots(1, 1, figsize=(4, 4))
ax = lensPlot.source_plot(ax=ax,
numPix=10,
deltaPix_source=0.1,
v_min=None,
v_max=None,
with_caustics=False,
caustic_color='yellow',
fsize=15,
plot_scale='bad')
plt.close()
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
background_rms=1)
# kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(
multi_band_list=[[kwargs_data, {
'psf_type': 'NONE'
}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
bands_compute=[False],
arrow_size=0.02,
cmap_string="gist_heat")
lensPlot._select_band(band_index=0)
with self.assertRaises(ValueError):
kwargs_data = sim_util.data_configure_simple(numPix=10,
deltaPix=1,
background_rms=1,
exposure_time=1)
# kwargs_data['image_data'] = np.zeros((10, 10))
kwargs_model = {'source_light_model_list': ['GAUSSIAN']}
kwargs_params = {
'kwargs_lens': [],
'kwargs_source': [{
'amp': 1,
'sigma': 1,
'center_x': 0,
'center_y': 0
}],
'kwargs_ps': [],
'kwargs_lens_light': []
}
lensPlot = ModelPlot(
multi_band_list=[[kwargs_data, {
'psf_type': 'NONE'
}, {}]],
kwargs_model=kwargs_model,
kwargs_params=kwargs_params,
bands_compute=[True],
arrow_size=0.02,
cmap_string="gist_heat")
f, ax = plt.subplots(1, 1, figsize=(4, 4))
ax = lensPlot.source_plot(ax=ax,
numPix=10,
deltaPix_source=0.1,
v_min=None,
v_max=None,
with_caustics=False,
caustic_color='yellow',
fsize=15,
plot_scale='wrong')
plt.close()
def generate_lens(sigma_bkg=sigma_bkg,
exp_time=exp_time,
numPix=numPix,
deltaPix=deltaPix,
fwhm=fwhm,
psf_type=psf_type,
kernel_size=kernel_size,
z_source=z_source,
z_lens=z_lens,
phi_ext=phi_ext,
gamma_ext=gamma_ext,
theta_E=theta_E,
gamma_lens=gamma_lens,
e1_lens=e1_lens,
e2_lens=e2_lens,
center_x_lens_light=center_x_lens_light,
center_y_lens_light=center_y_lens_light,
source_x=source_y,
source_y=source_y,
q_source=q_source,
phi_source=phi_source,
center_x=center_x,
center_y=center_y,
amp_source=amp_source,
R_sersic_source=R_sersic_source,
n_sersic_source=n_sersic_source,
phi_lens_light=phi_lens_light,
q_lens_light=q_lens_light,
amp_lens=amp_lens,
R_sersic_lens=R_sersic_lens,
n_sersic_lens=n_sersic_lens,
amp_ps=amp_ps,
supersampling_factor=supersampling_factor,
v_min=v_min,
v_max=v_max,
cosmo=cosmo,
cosmo2=cosmo2,
lens_pos_eq_lens_light_pos=True,
same_cosmology=True):
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time,
sigma_bkg)
data_class = ImageData(**kwargs_data)
kwargs_psf = {'psf_type': psf_type, 'pixel_size': deltaPix, 'fwhm': fwhm}
psf_class = PSF(**kwargs_psf)
cosmo = FlatLambdaCDM(H0=75, Om0=0.3, Ob0=0.)
cosmo = FlatLambdaCDM(H0=65, Om0=0.3, Ob0=0.)
gamma1, gamma2 = param_util.shear_polar2cartesian(phi=phi_ext,
gamma=gamma_ext)
kwargs_shear = {'gamma1': gamma1, 'gamma2': gamma2}
if lens_pos_eq_lens_light_pos:
center_x = center_x_lens_light
center_y = center_y_lens_light
if same_cosmology:
cosmo2 = cosmo
kwargs_spemd = {
'theta_E': theta_E,
'gamma': gamma_lens,
'center_x': center_x,
'center_y': center_y,
'e1': e1_lens,
'e2': e2_lens
}
lens_model_list = ['SPEP', 'SHEAR']
kwargs_lens = [kwargs_spemd, kwargs_shear]
lens_model_class = LensModel(lens_model_list=lens_model_list,
z_lens=z_lens,
z_source=z_source,
cosmo=cosmo)
lens_model_class2 = LensModel(lens_model_list=lens_model_list,
z_lens=z_lens,
z_source=z_source,
cosmo=cosmo2)
e1_source, e2_source = param_util.phi_q2_ellipticity(phi_source, q_source)
kwargs_sersic_source = {
'amp': amp_source,
'R_sersic': R_sersic_source,
'n_sersic': n_sersic_source,
'e1': e1_source,
'e2': e2_source,
'center_x': source_x,
'center_y': source_y
}
source_model_list = ['SERSIC_ELLIPSE']
kwargs_source = [kwargs_sersic_source]
source_model_class = LightModel(light_model_list=source_model_list)
##
e1_lens_light, e2_lens_light = param_util.phi_q2_ellipticity(
phi_lens_light, q_lens_light)
kwargs_sersic_lens = {
'amp': amp_lens,
'R_sersic': R_sersic_lens,
'n_sersic': n_sersic_lens,
'e1': e1_lens_light,
'e2': e2_lens_light,
'center_x': center_x_lens_light,
'center_y': center_y_lens_light
}
lens_light_model_list = ['SERSIC_ELLIPSE']
kwargs_lens_light = [kwargs_sersic_lens]
lens_light_model_class = LightModel(light_model_list=lens_light_model_list)
##
lensEquationSolver = LensEquationSolver(lens_model_class)
x_image, y_image = lensEquationSolver.findBrightImage(
source_x,
source_y, #position of ps
kwargs_lens, #lens proporties
numImages=4, #expected number of images
min_distance=deltaPix, #'resolution'
search_window=numPix * deltaPix) #search window limits
mag = lens_model_class.magnification(
x_image,
y_image, #for found above ps positions
kwargs=kwargs_lens) # and same lens properties
kwargs_ps = [{
'ra_image': x_image,
'dec_image': y_image,
'point_amp': np.abs(mag) * amp_ps
}]
point_source_list = ['LENSED_POSITION']
point_source_class = PointSource(point_source_type_list=point_source_list,
fixed_magnification_list=[False])
kwargs_numerics = {'supersampling_factor': supersampling_factor}
imageModel = ImageModel(
data_class, # take generated above data specs
psf_class, # same for psf
lens_model_class, # lens model (gal+ext)
source_model_class, # sourse light model
lens_light_model_class, # lens light model
point_source_class, # add generated ps images
kwargs_numerics=kwargs_numerics)
image_sim = imageModel.image(kwargs_lens, kwargs_source, kwargs_lens_light,
kwargs_ps)
poisson = image_util.add_poisson(image_sim, exp_time=exp_time)
bkg = image_util.add_background(image_sim, sigma_bkd=sigma_bkg)
image_sim = image_sim + bkg + poisson
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
cmap_string = 'gray'
cmap = plt.get_cmap(cmap_string)
cmap.set_bad(color='b', alpha=1.)
cmap.set_under('k')
f, axes = plt.subplots(1, 1, figsize=(6, 6), sharex=False, sharey=False)
ax = axes
im = ax.matshow(np.log10(image_sim),
origin='lower',
vmin=v_min,
vmax=v_max,
cmap=cmap,
extent=[0, 1, 0, 1])
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.autoscale(False)
plt.show()
kwargs_model = {
'lens_model_list': lens_model_list,
'lens_light_model_list': lens_light_model_list,
'source_light_model_list': source_model_list,
'point_source_model_list': point_source_list
}
from lenstronomy.Analysis.td_cosmography import TDCosmography
td_cosmo = TDCosmography(z_lens,
z_source,
kwargs_model,
cosmo_fiducial=cosmo)
# time delays, the unit [days] is matched when the lensing angles are in arcsec
t_days = td_cosmo.time_delays(kwargs_lens, kwargs_ps, kappa_ext=0)
dt_days = t_days[1:] - t_days[0]
dt_sigma = [3, 5, 10] # Gaussian errors
dt_measured = np.random.normal(dt_days, dt_sigma)
print("the measured relative delays are: ", dt_measured)
return f, source_model_list, kwargs_data, kwargs_psf, kwargs_numerics, dt_measured, dt_sigma, kwargs_ps, lens_model_list, lens_light_model_list, source_model_list, point_source_list, lens_model_class2
def setup(self):
# data specifics
sigma_bkg = .05 # background noise per pixel (Gaussian)
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 = 0.1 # full width half max of PSF (only valid when psf_type='gaussian')
psf_type = 'GAUSSIAN' # 'GAUSSIAN', 'PIXEL', 'NONE'
# generate the coordinate grid and image properties
kwargs_data = sim_util.data_configure_simple(numPix, deltaPix, exp_time, sigma_bkg)
kwargs_data['exposure_time'] = exp_time * np.ones_like(kwargs_data['image_data'])
data_class = ImageData(**kwargs_data)
# generate the psf variables
kwargs_psf = {'psf_type': psf_type, 'pixel_size': deltaPix, 'fwhm': fwhm}
# kwargs_psf = sim_util.psf_configure_simple(psf_type=psf_type, fwhm=fwhm, kernelsize=kernel_size, deltaPix=deltaPix, kernel=kernel)
psf_class = PSF(**kwargs_psf)
# lensing quantities
kwargs_shear = {'gamma1': 0.02, 'gamma2': -0.04} # shear values to the source plane
kwargs_spemd = {'theta_E': 1.26, 'gamma': 2., 'center_x': 0.0, 'center_y': 0.0, 'e1': -0.1,
'e2': 0.05} # parameters of the deflector lens model
# the lens model is a supperposition of an elliptical lens model with external shear
lens_model_list = ['EPL', 'SHEAR']
kwargs_lens_true = [kwargs_spemd, kwargs_shear]
lens_model_class = LensModel(lens_model_list=lens_model_list)
# choice of source type
source_type = 'SERSIC' # 'SERSIC' or 'SHAPELETS'
source_x = 0.
source_y = 0.05
# Sersic parameters in the initial simulation
phi_G, q = 0.5, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
kwargs_sersic_source = {'amp': 1000, 'R_sersic': 0.05, 'n_sersic': 1, 'e1': e1, 'e2': e2, 'center_x': source_x,
'center_y': source_y}
# kwargs_else = {'sourcePos_x': source_x, 'sourcePos_y': source_y, 'quasar_amp': 400., 'gamma1_foreground': 0.0, 'gamma2_foreground':-0.0}
source_model_list = ['SERSIC_ELLIPSE']
kwargs_source_true = [kwargs_sersic_source]
source_model_class = LightModel(light_model_list=source_model_list)
lensEquationSolver = LensEquationSolver(lens_model_class)
x_image, y_image = lensEquationSolver.findBrightImage(source_x, source_y, kwargs_lens_true, numImages=4,
min_distance=deltaPix, search_window=numPix * deltaPix)
mag = lens_model_class.magnification(x_image, y_image, kwargs=kwargs_lens_true)
kwargs_numerics = {'supersampling_factor': 1}
imageModel = ImageModel(data_class, psf_class, lens_model_class, source_model_class,
kwargs_numerics=kwargs_numerics)
# generate image
model = imageModel.image(kwargs_lens_true, kwargs_source_true)
poisson = image_util.add_poisson(model, exp_time=exp_time)
bkg = image_util.add_background(model, sigma_bkd=sigma_bkg)
image_sim = model + bkg + poisson
data_class.update_data(image_sim)
kwargs_data['image_data'] = image_sim
kwargs_model = {'lens_model_list': lens_model_list,
'source_light_model_list': source_model_list,
}
# make cutous and data instances of them
x_pos, y_pos = data_class.map_coord2pix(x_image, y_image)
ra_grid, dec_grid = data_class.pixel_coordinates
multi_band_list = []
for i in range(len(x_pos)):
n_cut = 12
x_c = int(x_pos[i])
y_c = int(y_pos[i])
image_cut = image_sim[int(y_c - n_cut):int(y_c + n_cut), int(x_c - n_cut):int(x_c + n_cut)]
exposure_map_cut = data_class.exposure_map[int(y_c - n_cut):int(y_c + n_cut),
int(x_c - n_cut):int(x_c + n_cut)]
kwargs_data_i = {
'background_rms': data_class.background_rms,
'exposure_time': exposure_map_cut,
'ra_at_xy_0': ra_grid[y_c - n_cut, x_c - n_cut], 'dec_at_xy_0': dec_grid[y_c - n_cut, x_c - n_cut],
'transform_pix2angle': data_class.transform_pix2angle
, 'image_data': image_cut
}
multi_band_list.append([kwargs_data_i, kwargs_psf, kwargs_numerics])
kwargs_params = {'kwargs_lens': kwargs_lens_true, 'kwargs_source': kwargs_source_true}
self.multiPatch = MultiPatchPlot(multi_band_list, kwargs_model, kwargs_params, multi_band_type='joint-linear',
kwargs_likelihood=None, verbose=True, cmap_string="gist_heat")
self.data_class = data_class
self.model = model
self.lens_model_class = lens_model_class
self.kwargs_lens = kwargs_lens_true