def __init__(self, light_model_list, smoothing=0.0000001):
self.profile_type_list = light_model_list
self.func_list = []
for profile_type in light_model_list:
valid = True
if profile_type == 'GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
self.func_list.append(Gaussian())
elif profile_type == 'GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import GaussianEllipse
self.func_list.append(GaussianEllipse())
elif profile_type == 'MULTI_GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussian
self.func_list.append(MultiGaussian())
elif profile_type == 'MULTI_GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussianEllipse
self.func_list.append(MultiGaussianEllipse())
elif profile_type == 'SERSIC':
from lenstronomy.LightModel.Profiles.sersic import Sersic
self.func_list.append(Sersic(smoothing=smoothing))
elif profile_type == 'SERSIC_ELLIPSE':
from lenstronomy.LightModel.Profiles.sersic import Sersic_elliptic
self.func_list.append(Sersic_elliptic(smoothing=smoothing))
elif profile_type == 'CORE_SERSIC':
from lenstronomy.LightModel.Profiles.sersic import CoreSersic
self.func_list.append(CoreSersic(smoothing=smoothing))
elif profile_type == 'SHAPELETS':
from lenstronomy.LightModel.Profiles.shapelets import ShapeletSet
self.func_list.append(ShapeletSet())
elif profile_type == 'HERNQUIST':
from lenstronomy.LightModel.Profiles.hernquist import Hernquist
self.func_list.append(Hernquist())
elif profile_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LightModel.Profiles.hernquist import Hernquist_Ellipse
self.func_list.append(Hernquist_Ellipse())
elif profile_type == 'PJAFFE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe
self.func_list.append(PJaffe())
elif profile_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe_Ellipse
self.func_list.append(PJaffe_Ellipse())
elif profile_type == 'UNIFORM':
from lenstronomy.LightModel.Profiles.uniform import Uniform
self.func_list.append(Uniform())
elif profile_type == 'POWER_LAW':
from lenstronomy.LightModel.Profiles.power_law import PowerLaw
self.func_list.append(PowerLaw())
elif profile_type == 'NIE':
from lenstronomy.LightModel.Profiles.nie import NIE
self.func_list.append(NIE())
elif profile_type == 'CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import Chameleon
self.func_list.append(Chameleon())
elif profile_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import DoubleChameleon
self.func_list.append(DoubleChameleon())
else:
raise ValueError('Warning! No light model of type', profile_type, ' found!')
class TestShapeletSet(object):
"""
class to test Shapelets
"""
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
def test_shapelet_set(self):
"""
:return:
"""
n_max = 2
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
input = np.array(0.)
input += output
output = self.shapeletSet.function(self.x,
self.y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output[10] == 0.47957022395315946
output = self.shapeletSet.function(1,
1,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
n_max = -1
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0
beta = 1.
amp = 1
shapelets = Shapelets(precalc=False, stable_cut=False)
output = shapelets.function(np.array(1),
np.array(1),
amp,
beta,
0,
0,
center_x=0,
center_y=0)
npt.assert_almost_equal(0.2075537487102974, output, decimal=8)
def test_shapelet_basis(self):
num_order = 5
beta = 1
numPix = 10
kernel_list = self.shapeletSet.shapelet_basis_2d(
num_order, beta, numPix)
assert kernel_list[0][4, 4] == 0.43939128946772255
def test_decomposition(self):
"""
:return:
"""
n_max = 2
beta = 10.
deltaPix = 1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(100, deltaPix, 1)
input = self.shapeletSet.function(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
amp_out = self.shapeletSet.decomposition(input,
x,
y,
n_max,
beta,
deltaPix,
center_x=0,
center_y=0)
for i in range(len(amp)):
npt.assert_almost_equal(amp_out[i], amp[i], decimal=4)
def test_function_split(self):
n_max = 2
beta = 10.
deltaPix = 0.1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(10, deltaPix, 1)
function_set = self.shapeletSet.function_split(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
test_flux = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
print(np.shape(function_set))
print(np.shape(test_flux))
assert function_set[0][10] == test_flux[10]
def test_interpolate(self):
shapeletsInterp = Shapelets(interpolation=True)
x, y = 0.99, 0
beta = 0.5
flux_full = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
flux_interp = shapeletsInterp.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
npt.assert_almost_equal(flux_interp, flux_full, decimal=10)
def test_hermval(self):
x = np.linspace(0, 2000, 2001)
n_array = [1, 2, 3, 0, 1]
import numpy.polynomial.hermite as hermite
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
shape_true = out_true * np.exp(-x**2 / 2.)
shape_approx = out_approx * np.exp(-x**2 / 2.)
npt.assert_almost_equal(shape_approx, shape_true, decimal=6)
x = 2
n_array = [1, 2, 3, 0, 1]
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
npt.assert_almost_equal(out_approx, out_true, decimal=6)
x = 2001
n_array = [1, 2, 3, 0, 1]
out_true = hermite.hermval(x, n_array)
out_approx = self.shapelets.hermval(x, n_array)
shape_true = out_true * np.exp(-x**2 / 2.)
shape_approx = out_approx * np.exp(-x**2 / 2.)
npt.assert_almost_equal(shape_approx, shape_true, decimal=6)
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
def __init__(self, light_model_list, smoothing=0.001):
"""
:param light_model_list: list of light models
:param smoothing: smoothing factor for certain models (deprecated)
"""
self.profile_type_list = light_model_list
self.func_list = []
for profile_type in light_model_list:
if profile_type == 'GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
self.func_list.append(Gaussian())
elif profile_type == 'GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import GaussianEllipse
self.func_list.append(GaussianEllipse())
elif profile_type == 'ELLIPSOID':
from lenstronomy.LightModel.Profiles.ellipsoid import Ellipsoid
self.func_list.append(Ellipsoid())
elif profile_type == 'MULTI_GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussian
self.func_list.append(MultiGaussian())
elif profile_type == 'MULTI_GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussianEllipse
self.func_list.append(MultiGaussianEllipse())
elif profile_type == 'SERSIC':
from lenstronomy.LightModel.Profiles.sersic import Sersic
self.func_list.append(Sersic(smoothing=smoothing))
elif profile_type == 'SERSIC_ELLIPSE':
from lenstronomy.LightModel.Profiles.sersic import SersicElliptic
self.func_list.append(
SersicElliptic(smoothing=smoothing,
sersic_major_axis=sersic_major_axis_conf))
elif profile_type == 'CORE_SERSIC':
from lenstronomy.LightModel.Profiles.sersic import CoreSersic
self.func_list.append(CoreSersic(smoothing=smoothing))
elif profile_type == 'SHAPELETS':
from lenstronomy.LightModel.Profiles.shapelets import ShapeletSet
self.func_list.append(ShapeletSet())
elif profile_type == 'SHAPELETS_POLAR':
from lenstronomy.LightModel.Profiles.shapelets_polar import ShapeletSetPolar
self.func_list.append(ShapeletSetPolar(exponential=False))
elif profile_type == 'SHAPELETS_POLAR_EXP':
from lenstronomy.LightModel.Profiles.shapelets_polar import ShapeletSetPolar
self.func_list.append(ShapeletSetPolar(exponential=True))
elif profile_type == 'HERNQUIST':
from lenstronomy.LightModel.Profiles.hernquist import Hernquist
self.func_list.append(Hernquist())
elif profile_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LightModel.Profiles.hernquist import HernquistEllipse
self.func_list.append(HernquistEllipse())
elif profile_type == 'PJAFFE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe
self.func_list.append(PJaffe())
elif profile_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe_Ellipse
self.func_list.append(PJaffe_Ellipse())
elif profile_type == 'UNIFORM':
from lenstronomy.LightModel.Profiles.uniform import Uniform
self.func_list.append(Uniform())
elif profile_type == 'POWER_LAW':
from lenstronomy.LightModel.Profiles.power_law import PowerLaw
self.func_list.append(PowerLaw())
elif profile_type == 'NIE':
from lenstronomy.LightModel.Profiles.nie import NIE
self.func_list.append(NIE())
elif profile_type == 'CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import Chameleon
self.func_list.append(Chameleon())
elif profile_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import DoubleChameleon
self.func_list.append(DoubleChameleon())
elif profile_type == 'TRIPLE_CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import TripleChameleon
self.func_list.append(TripleChameleon())
elif profile_type == 'INTERPOL':
from lenstronomy.LightModel.Profiles.interpolation import Interpol
self.func_list.append(Interpol())
elif profile_type == 'SLIT_STARLETS':
from lenstronomy.LightModel.Profiles.starlets import SLIT_Starlets
self.func_list.append(
SLIT_Starlets(fast_inverse=True, second_gen=False))
elif profile_type == 'SLIT_STARLETS_GEN2':
from lenstronomy.LightModel.Profiles.starlets import SLIT_Starlets
self.func_list.append(SLIT_Starlets(second_gen=True))
else:
raise ValueError(
'No light model of type %s found! Supported are the following models: %s'
% (profile_type, _MODELS_SUPPORTED))
self._num_func = len(self.func_list)
x, y = util.make_grid(numPix=numPix_large - 1,
deltapix=1) # make a coordinate grid
ngc_data_resized = image_util.re_size(
ngc_cut, factor) # re-size image to lower resolution
# now we come to the Shapelet decomposition
# we turn the image in a single 1d array
image_1d = util.image2array(ngc_data_resized) # map 2d image in 1d data array
# we define the shapelet basis set we want the image to decompose in
n_max = 150 # choice of number of shapelet basis functions, 150 is a high resolution number, but takes long
beta = 10 # shapelet scale parameter (in units of resized pixels)
# import the ShapeletSet class
from lenstronomy.LightModel.Profiles.shapelets import ShapeletSet
shapeletSet = ShapeletSet()
# decompose image and return the shapelet coefficients
coeff_ngc = shapeletSet.decomposition(image_1d,
x,
y,
n_max,
beta,
1.,
center_x=0,
center_y=0)
print(len(coeff_ngc), 'number of coefficients') # number of coefficients
# reconstruct NGC1300 with the shapelet coefficients
image_reconstructed = shapeletSet.function(x,
y,
from lenstronomy.SimulationAPI.simulations import Simulation
SimAPI = Simulation()
from lenstronomy.LightModel.light_model import LightModel
from lenstronomy.LensModel.lens_model import LensModel
import lenstronomy.Plots.output_plots as lens_plot
from lenstronomy.LensModel.Solver.lens_equation_solver import LensEquationSolver
import lenstronomy.Util.param_util as param_util
from lenstronomy.PointSource.point_source import PointSource
from lenstronomy.ImSim.image_model import ImageModel
import lenstronomy.Util.image_util as image_util
from lenstronomy.Plots.output_plots import LensModelPlot
from lenstronomy.Workflow.fitting_sequence import FittingSequence
import lenstronomy.Plots.output_plots as out_plot
import lenstronomy.Util.util as util
from lenstronomy.LightModel.Profiles.shapelets import ShapeletSet
shapeletSet = ShapeletSet()
#********************************************************************************#
# DES CATALOGUE #
#********************************************************************************#
cat0=np.loadtxt('gal.npy').reshape(6,111,110) #is needed give the right shape to the file
pix=110 # to have squared images
cat= cat0[:,0:pix,0:pix]
# To Create a mask around the central object
masksize=40 #mask size in pixels
msmin = int(pix/2) - int(masksize/2)
msmax = int(pix/2) + int(masksize/2)
catmask = cat[:,msmin:msmax,msmin:msmax]
class TestShapeletSet(object):
"""
class to test Shapelets
"""
def setup(self):
self.shapeletSet = ShapeletSet()
self.shapelets = Shapelets(precalc=False)
self.x, self.y = util.make_grid(10, 0.1, 1)
def test_shapelet_set(self):
"""
:return:
"""
n_max = 2
beta = 1.
amp = [1, 0, 0, 0, 0, 0]
output = self.shapeletSet.function(np.array(1),
np.array(1),
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
input = np.array(0.)
input += output
output = self.shapeletSet.function(self.x,
self.y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output[10] == 0.47957022395315946
output = self.shapeletSet.function(1,
1,
amp,
n_max,
beta,
center_x=0,
center_y=0)
assert output == 0.20755374871029739
def test_shapelet_basis(self):
num_order = 5
beta = 1
numPix = 10
kernel_list = self.shapeletSet.shapelet_basis_2d(
num_order, beta, numPix)
assert kernel_list[0][4, 4] == 0.43939128946772255
def test_decomposition(self):
"""
:return:
"""
n_max = 2
beta = 10.
deltaPix = 1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(100, deltaPix, 1)
input = self.shapeletSet.function(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
amp_out = self.shapeletSet.decomposition(input,
x,
y,
n_max,
beta,
deltaPix,
center_x=0,
center_y=0)
for i in range(len(amp)):
npt.assert_almost_equal(amp_out[i], amp[i], decimal=4)
def test_function_split(self):
n_max = 2
beta = 10.
deltaPix = 0.1
amp = np.array([1, 1, 1, 1, 1, 1])
x, y = util.make_grid(10, deltaPix, 1)
function_set = self.shapeletSet.function_split(x,
y,
amp,
n_max,
beta,
center_x=0,
center_y=0)
test_flux = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
print(np.shape(function_set))
print(np.shape(test_flux))
assert function_set[0][10] == test_flux[10]
def test_interpolate(self):
shapeletsInterp = Shapelets(interpolation=True)
x, y = 0.99, 0
beta = 0.5
flux_full = self.shapelets.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
flux_interp = shapeletsInterp.function(x,
y,
amp=1.,
n1=0,
n2=0,
beta=beta,
center_x=0,
center_y=0)
npt.assert_almost_equal(flux_interp, flux_full, decimal=10)
def __init__(self,
light_model_list,
deflection_scaling_list=None,
source_redshift_list=None,
smoothing=0.0000001):
"""
:param light_model_list: list of light models
:param deflection_scaling_list: list of floats, rescales the original reduced deflection angles from the lens model
to enable different models to be placed at different optical (redshift) distances. None means they are all
:param source_redshift_list: list of redshifts of the model components
:param smoothing: smoothing factor for certain models (deprecated)
"""
self.profile_type_list = light_model_list
self.deflection_scaling_list = deflection_scaling_list
self.redshift_list = source_redshift_list
self.func_list = []
for profile_type in light_model_list:
if profile_type == 'GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import Gaussian
self.func_list.append(Gaussian())
elif profile_type == 'GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import GaussianEllipse
self.func_list.append(GaussianEllipse())
elif profile_type == 'MULTI_GAUSSIAN':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussian
self.func_list.append(MultiGaussian())
elif profile_type == 'MULTI_GAUSSIAN_ELLIPSE':
from lenstronomy.LightModel.Profiles.gaussian import MultiGaussianEllipse
self.func_list.append(MultiGaussianEllipse())
elif profile_type == 'SERSIC':
from lenstronomy.LightModel.Profiles.sersic import Sersic
self.func_list.append(Sersic(smoothing=smoothing))
elif profile_type == 'SERSIC_ELLIPSE':
from lenstronomy.LightModel.Profiles.sersic import SersicElliptic
self.func_list.append(SersicElliptic(smoothing=smoothing))
elif profile_type == 'CORE_SERSIC':
from lenstronomy.LightModel.Profiles.sersic import CoreSersic
self.func_list.append(CoreSersic(smoothing=smoothing))
elif profile_type == 'SHAPELETS':
from lenstronomy.LightModel.Profiles.shapelets import ShapeletSet
self.func_list.append(ShapeletSet())
elif profile_type == 'HERNQUIST':
from lenstronomy.LightModel.Profiles.hernquist import Hernquist
self.func_list.append(Hernquist())
elif profile_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LightModel.Profiles.hernquist import HernquistEllipse
self.func_list.append(HernquistEllipse())
elif profile_type == 'PJAFFE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe
self.func_list.append(PJaffe())
elif profile_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LightModel.Profiles.p_jaffe import PJaffe_Ellipse
self.func_list.append(PJaffe_Ellipse())
elif profile_type == 'UNIFORM':
from lenstronomy.LightModel.Profiles.uniform import Uniform
self.func_list.append(Uniform())
elif profile_type == 'POWER_LAW':
from lenstronomy.LightModel.Profiles.power_law import PowerLaw
self.func_list.append(PowerLaw())
elif profile_type == 'NIE':
from lenstronomy.LightModel.Profiles.nie import NIE
self.func_list.append(NIE())
elif profile_type == 'CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import Chameleon
self.func_list.append(Chameleon())
elif profile_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LightModel.Profiles.chameleon import DoubleChameleon
self.func_list.append(DoubleChameleon())
elif profile_type == 'INTERPOL':
from lenstronomy.LightModel.Profiles.interpolation import Interpol
self.func_list.append(Interpol())
else:
raise ValueError('Warning! No light model of type',
profile_type, ' found!')
