def sim_image(numpix,
kwargs_band,
kwargs_model,
kwargs_params,
kwargs_numerics={},
with_noise=True):
"""
simulates an image based on chosen model and data settings, effectively makes use of lenstronomy.SimulationAPI
:param numpix: number of pixels per axis
:param kwargs_band: keyword arguments specifying the observation to be simulated according to lenstronomy.SimulationAPI
:param kwargs_model: keyword arguments of model configurations. All possibilities available at lenstronom.Util.class_creator
:param kwargs_params: keyword arguments of the different model components. Supports 'kwargs_lens', 'kwargs_source_mag',
'kwargs_lens_light_mag', 'kwargs_ps_mag'
:param kwargs_numerics: keyword arguments describing the numerical setting of lenstronomy as outlined in lenstronomy.ImSim.Numerics
:return: 2d numpy array
"""
sim = SimAPI(numpix=numpix,
kwargs_single_band=kwargs_band,
kwargs_model=kwargs_model,
kwargs_numerics=kwargs_numerics)
kwargs_lens_light_mag = kwargs_params.get('kwargs_lens_light_mag', None)
kwargs_source_mag = kwargs_params.get('kwargs_source_mag', None)
kwargs_ps_mag = kwargs_params.get('kwargs_ps_mag', None)
kwargs_lens_light, kwargs_source, kwargs_ps = sim.magnitude2amplitude(
kwargs_lens_light_mag, kwargs_source_mag, kwargs_ps_mag)
imSim = sim.image_model_class
image = imSim.image(kwargs_params['kwargs_lens'], kwargs_source,
kwargs_lens_light, kwargs_ps)
if with_noise is True:
image += sim.noise_for_model(model=image)
return image
def sim_single_image(sn_data, geo_dict, label):
DECam = {
'read_noise':
10, # std of noise generated by read-out (in units of electrons)
'pixel_scale':
0.263, # scale (in arcseonds) of pixels
'ccd_gain':
4.5 # electrons/ADU (analog-to-digital unit).
}
obs_info = {
'exposure_time': 90,
'sky_brightness': sn_data['SKYMAG'],
'magnitude_zero_point': sn_data['ZPTMAG'],
'num_exposures': 1,
'seeing': sn_data['PSF'] / 3,
'psf_type': 'GAUSSIAN'
}
kwargs_model = {
'lens_model_list': [
'SIE', 'SHEAR'
], # list of lens models to be used
'lens_light_model_list': [
'SERSIC_ELLIPSE'
], # list of unlensed light models to be used
'source_light_model_list': [
'SERSIC_ELLIPSE'
], # list of extended source models to be used
'point_source_model_list': [
'SOURCE_POSITION'
], # list of point source models to be used
}
numpix = 64
kwargs_merged = util.merge_dicts(DECam, obs_info)
kwargs_numerics = {'point_source_supersampling_factor': 1}
sim = SimAPI(numpix=numpix,
kwargs_single_band=kwargs_merged,
kwargs_model=kwargs_model,
kwargs_numerics=kwargs_numerics)
imSim = sim.image_model_class
x_grid, y_grid = util.make_grid(numPix=10, deltapix=1)
flux = gauss.function(x_grid,
y_grid,
amp=1,
sigma=2,
e1=0.4,
e2=0,
center_x=0,
center_y=0)
image_gauss = util.array2image(flux)
## Set point source mag
geo_dict['ps_kwargs']['magnitude'] = sn_data['ABMAG'] * 1.4
if label == 1:
geo_dict = force_larger_ps_separation(geo_dict)
elif label == 2:
geo_dict = force_no_lensing_with_ps(geo_dict)
elif label == 3:
geo_dict = force_lensed_agn(geo_dict)
elif label == 4:
geo_dict = force_non_lensed_agn(geo_dict)
else:
print("Unexpected option passed as class label")
sys.exit()
#lens light
kwargs_lens_light_mag = [geo_dict['lens_kwargs']]
# source light
kwargs_source_mag = [geo_dict['source_kwargs']]
# point source
kwargs_ps_mag = [geo_dict['ps_kwargs']]
kwargs_lens_light, kwargs_source, kwargs_ps = sim.magnitude2amplitude(
kwargs_lens_light_mag, kwargs_source_mag, kwargs_ps_mag)
kwargs_lens = [
geo_dict['lens_model_kwargs_sie'], geo_dict['lens_model_kwargs_shear']
]
image = imSim.image(kwargs_lens, kwargs_source, kwargs_lens_light,
kwargs_ps)
image += sim.noise_for_model(model=image)
return np.log10(image + 2)