def mcmc(self,
n_burn,
n_run,
walkerRatio,
sigma_scale=1,
threadCount=1,
init_samples=None,
re_use_samples=True):
"""
MCMC routine
:param n_burn: number of burn in iterations (will not be saved)
:param n_run: number of MCMC iterations that are saved
:param walkerRatio: ratio of walkers/number of free parameters
:param sigma_scale: scaling of the initial parameter spread relative to the width in the initial settings
:param threadCount: number of CPU threads. If MPI option is set, threadCount=1
:param init_samples: initial sample from where to start the MCMC process
:param re_use_samples: bool, if True, re-uses the samples described in init_samples.nOtherwise starts from scratch.
:return: MCMC samples, parameter names, logL distances of all samples
"""
param_class = self._param_class
# run PSO
mcmc_class = Sampler(likelihoodModule=self.likelihoodModule)
mean_start = param_class.kwargs2args(self._lens_temp,
self._source_temp,
self._lens_light_temp,
self._ps_temp, self._cosmo_temp)
lens_sigma, source_sigma, lens_light_sigma, ps_sigma, cosmo_sigma = self._updateManager.sigma_kwargs
sigma_start = param_class.kwargs2args(lens_sigma, source_sigma,
lens_light_sigma, ps_sigma,
cosmo_sigma)
num_param, param_list = param_class.num_param()
# run MCMC
if not init_samples is None and re_use_samples is True:
print("test that you are here!")
num_samples, num_param_prev = np.shape(init_samples)
print(num_samples, num_param_prev, num_param,
'shape of init_sample')
if num_param_prev == num_param:
print(
"re-using previous samples to initialize the next MCMC run."
)
initpos = ReusePositionGenerator(init_samples)
else:
print(
"Can not re-use previous MCMC samples due to change in option"
)
initpos = None
else:
initpos = None
samples, dist = mcmc_class.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
np.array(sigma_start) * sigma_scale,
threadCount=threadCount,
mpi=self._mpi,
init_pos=initpos)
return samples, param_list, dist
def pso(self, n_particles, n_iterations, sigma_scale=1, print_key='PSO', threadCount=1):
"""
Particle Swarm Optimization
:param n_particles: number of particles in the Particle Swarm Optimization
:param n_iterations: number of iterations in the optimization process
:param sigma_scale: scaling of the initial parameter spread relative to the width in the initial settings
:param print_key: string, printed text when executing this routine
:param threadCount: number of CPU threads. If MPI option is set, threadCount=1
:return: result of the best fit, the chain of the best fit parameter after each iteration, list of parameters in same order
"""
param_class = self.param_class
kwargs_temp = self._updateManager.parameter_state
init_pos = param_class.kwargs2args(**kwargs_temp)
kwargs_sigma = self._updateManager.sigma_kwargs
sigma_start = param_class.kwargs2args(**kwargs_sigma)
lowerLimit = np.array(init_pos) - np.array(sigma_start) * sigma_scale
upperLimit = np.array(init_pos) + np.array(sigma_start) * sigma_scale
num_param, param_list = param_class.num_param()
# run PSO
sampler = Sampler(likelihoodModule=self.likelihoodModule)
result, chain = sampler.pso(n_particles, n_iterations, lowerLimit, upperLimit, init_pos=init_pos,
threadCount=threadCount, mpi=self._mpi, print_key=print_key)
kwargs_result = param_class.args2kwargs(result, bijective=True)
return kwargs_result, chain, param_list
def mcmc(self, n_burn, n_run, walkerRatio, sigma_scale=1, threadCount=1, init_samples=None, re_use_samples=True,
sampler_type='EMCEE', progress=True, backup_filename=None, start_from_backup=False):
"""
MCMC routine
:param n_burn: number of burn in iterations (will not be saved)
:param n_run: number of MCMC iterations that are saved
:param walkerRatio: ratio of walkers/number of free parameters
:param sigma_scale: scaling of the initial parameter spread relative to the width in the initial settings
:param threadCount: number of CPU threads. If MPI option is set, threadCount=1
:param init_samples: initial sample from where to start the MCMC process
:param re_use_samples: bool, if True, re-uses the samples described in init_samples.nOtherwise starts from scratch.
:param sampler_type: string, which MCMC sampler to be used. Options are: 'EMCEE'
:param progress: boolean, if True shows progress bar in EMCEE
:return: list of output arguments, e.g. MCMC samples, parameter names, logL distances of all samples specified by the specific sampler used
"""
param_class = self.param_class
# run PSO
mcmc_class = Sampler(likelihoodModule=self.likelihoodModule)
kwargs_temp = self._updateManager.parameter_state
mean_start = param_class.kwargs2args(**kwargs_temp)
kwargs_sigma = self._updateManager.sigma_kwargs
sigma_start = np.array(param_class.kwargs2args(**kwargs_sigma)) * sigma_scale
num_param, param_list = param_class.num_param()
# run MCMC
if not init_samples is None and re_use_samples is True:
num_samples, num_param_prev = np.shape(init_samples)
if num_param_prev == num_param:
print("re-using previous samples to initialize the next MCMC run.")
n_walkers = num_param * walkerRatio
idxs = np.random.choice(len(init_samples), n_walkers)
initpos = init_samples[idxs]
else:
raise ValueError("Can not re-use previous MCMC samples as number of parameters have changed!")
else:
initpos = None
if sampler_type == 'EMCEE':
n_walkers = num_param * walkerRatio
samples, dist = mcmc_class.mcmc_emcee(n_walkers, n_run, n_burn, mean_start, sigma_start, mpi=self._mpi,
threadCount=threadCount, progress=progress, initpos=initpos,
backup_filename=backup_filename, start_from_backup=start_from_backup)
output = [sampler_type, samples, param_list, dist]
else:
raise ValueError('sampler_type %s not supported!' % sampler_type)
self._mcmc_init_samples = samples # overwrites previous samples to continue from there in the next MCMC run
return output
def simplex(self, n_iterations, method='Nelder-Mead'):
"""
Downhill simplex optimization using the Nelder-Mead algorithm.
:param n_iterations: maximum number of iterations to perform
:param method: the optimization method used, see documentation in scipy.optimize.minimize
:return: result of the best fit
"""
param_class = self.param_class
kwargs_temp = self._updateManager.parameter_state
init_pos = param_class.kwargs2args(**kwargs_temp)
sampler = Sampler(likelihoodModule=self.likelihoodModule)
result = sampler.simplex(init_pos, n_iterations, method)
kwargs_result = param_class.args2kwargs(result, bijective=True)
return kwargs_result
def _run_pso(self,
n_particles,
n_iterations,
kwargs_fixed_lens,
kwargs_mean_lens,
kwargs_sigma_lens,
kwargs_fixed_source,
kwargs_mean_source,
kwargs_sigma_source,
kwargs_fixed_lens_light,
kwargs_mean_lens_light,
kwargs_sigma_lens_light,
kwargs_fixed_ps,
kwargs_mean_ps,
kwargs_sigma_ps,
kwargs_fixed_cosmo,
kwargs_mean_cosmo,
kwargs_sigma_cosmo,
threadCount=1,
mpi=False,
print_key='PSO',
sigma_factor=1,
compute_bool=None,
fix_solver=False):
# initialise mcmc classes
param_class = Param(self.kwargs_model,
self.kwargs_constraints,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
self._lens_lower,
self._source_lower,
self._lens_light_lower,
self._ps_lower,
self._cosmo_lower,
self._lens_upper,
self._source_upper,
self._lens_light_upper,
self._ps_upper,
self._cosmo_upper,
kwargs_lens_init=kwargs_mean_lens,
fix_lens_solver=fix_solver)
init_pos = param_class.setParams(kwargs_mean_lens, kwargs_mean_source,
kwargs_mean_lens_light,
kwargs_mean_ps, kwargs_mean_cosmo)
sigma_start = param_class.setParams(kwargs_sigma_lens,
kwargs_sigma_source,
kwargs_sigma_lens_light,
kwargs_sigma_ps,
kwargs_sigma_cosmo)
lowerLimit = np.array(init_pos) - np.array(sigma_start) * sigma_factor
upperLimit = np.array(init_pos) + np.array(sigma_start) * sigma_factor
num_param, param_list = param_class.num_param()
# initialize ImSim() class
kwargs_likelihood = copy.deepcopy(self.kwargs_likelihood)
if compute_bool is not None:
kwargs_likelihood['bands_compute'] = compute_bool
imSim_class = class_creator.create_multiband(self.multi_band_list,
self.kwargs_model)
likelihoodModule = LikelihoodModule(
imSim_class=imSim_class,
param_class=param_class,
kwargs_likelihood=kwargs_likelihood)
# run PSO
mcmc_class = Sampler(likelihoodModule=likelihoodModule)
result, chain = mcmc_class.pso(n_particles,
n_iterations,
lowerLimit,
upperLimit,
init_pos=init_pos,
threadCount=threadCount,
mpi=mpi,
print_key=print_key)
lens_result, source_result, lens_light_result, ps_result, cosmo_result = param_class.getParams(
result, bijective=True)
return lens_result, source_result, lens_light_result, ps_result, cosmo_result, chain, param_list
def _mcmc_run(self,
n_burn,
n_run,
walkerRatio,
kwargs_fixed_lens,
kwargs_mean_lens,
kwargs_sigma_lens,
kwargs_fixed_source,
kwargs_mean_source,
kwargs_sigma_source,
kwargs_fixed_lens_light,
kwargs_mean_lens_light,
kwargs_sigma_lens_light,
kwargs_fixed_ps,
kwargs_mean_ps,
kwargs_sigma_ps,
kwargs_fixed_cosmo,
kwargs_mean_cosmo,
kwargs_sigma_cosmo,
threadCount=1,
mpi=False,
init_samples=None,
sigma_factor=1,
compute_bool=None,
fix_solver=False):
param_class = Param(self.kwargs_model,
self.kwargs_constraints,
kwargs_fixed_lens,
kwargs_fixed_source,
kwargs_fixed_lens_light,
kwargs_fixed_ps,
kwargs_fixed_cosmo,
self._lens_lower,
self._source_lower,
self._lens_light_lower,
self._ps_lower,
self._cosmo_lower,
self._lens_upper,
self._source_upper,
self._lens_light_upper,
self._ps_upper,
self._cosmo_upper,
kwargs_lens_init=kwargs_mean_lens,
fix_lens_solver=fix_solver)
# initialize ImSim() class
kwargs_likelihood = copy.deepcopy(self.kwargs_likelihood)
if compute_bool is not None:
kwargs_likelihood['bands_compute'] = compute_bool
imSim_class = class_creator.create_multiband(self.multi_band_list,
self.kwargs_model)
likelihoodModule = LikelihoodModule(
imSim_class=imSim_class,
param_class=param_class,
kwargs_likelihood=kwargs_likelihood)
# run PSO
mcmc_class = Sampler(likelihoodModule=likelihoodModule)
mean_start = param_class.setParams(kwargs_mean_lens,
kwargs_mean_source,
kwargs_mean_lens_light,
kwargs_mean_ps, kwargs_mean_cosmo)
sigma_start = param_class.setParams(kwargs_sigma_lens,
kwargs_sigma_source,
kwargs_sigma_lens_light,
kwargs_sigma_ps,
kwargs_sigma_cosmo)
num_param, param_list = param_class.num_param()
# run MCMC
if not init_samples is None:
initpos = ReusePositionGenerator(init_samples)
else:
initpos = None
samples, dist = mcmc_class.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
np.array(sigma_start) *
sigma_factor,
threadCount=threadCount,
mpi=mpi,
init_pos=initpos)
return samples, param_list, dist
def setup(self):
self.SimAPI = Simulation()
# 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 = self.SimAPI.data_configure(numPix, deltaPix, exp_time,
sigma_bkg)
data_class = Data(kwargs_data)
kwargs_psf = self.SimAPI.psf_configure(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=3,
kernel=None)
kwargs_psf = self.SimAPI.psf_configure(
psf_type='PIXEL',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=6,
kernel=kwargs_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 = {'subgrid_res': 1, 'psf_subgrid': False}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = self.SimAPI.simulate(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light)
data_class.update_data(image_sim)
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}
num_source_model = len(source_model_list)
kwargs_constraints = {
'joint_center_lens_light': False,
'joint_center_source_light': False,
'additional_images_list': [False],
'fix_to_point_source_list': [False] * num_source_model,
'image_plane_source_list': [False] * num_source_model,
'solver': False,
}
kwargs_likelihood = {
'source_marg': True,
'point_source_likelihood': False,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
self.param_class = Param(kwargs_model, kwargs_constraints)
self.Likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=self.param_class,
kwargs_likelihood=kwargs_likelihood)
self.sampler = Sampler(likelihoodModule=self.Likelihood)
class TestFittingSequence(object):
"""
test the fitting sequences
"""
def setup(self):
self.SimAPI = Simulation()
# 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 = self.SimAPI.data_configure(numPix, deltaPix, exp_time,
sigma_bkg)
data_class = Data(kwargs_data)
kwargs_psf = self.SimAPI.psf_configure(psf_type='GAUSSIAN',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=3,
kernel=None)
kwargs_psf = self.SimAPI.psf_configure(
psf_type='PIXEL',
fwhm=fwhm,
kernelsize=11,
deltaPix=deltaPix,
truncate=6,
kernel=kwargs_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 = {'subgrid_res': 1, 'psf_subgrid': False}
imageModel = ImageModel(data_class,
psf_class,
lens_model_class,
source_model_class,
lens_light_model_class,
kwargs_numerics=kwargs_numerics)
image_sim = self.SimAPI.simulate(imageModel, self.kwargs_lens,
self.kwargs_source,
self.kwargs_lens_light)
data_class.update_data(image_sim)
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}
num_source_model = len(source_model_list)
kwargs_constraints = {
'joint_center_lens_light': False,
'joint_center_source_light': False,
'additional_images_list': [False],
'fix_to_point_source_list': [False] * num_source_model,
'image_plane_source_list': [False] * num_source_model,
'solver': False,
}
kwargs_likelihood = {
'source_marg': True,
'point_source_likelihood': False,
'position_uncertainty': 0.004,
'check_solver': False,
'solver_tolerance': 0.001,
}
self.param_class = Param(kwargs_model, kwargs_constraints)
self.Likelihood = LikelihoodModule(imSim_class=imageModel,
param_class=self.param_class,
kwargs_likelihood=kwargs_likelihood)
self.sampler = Sampler(likelihoodModule=self.Likelihood)
def test_pso(self):
n_particles = 2
n_iterations = 2
result, chain = self.sampler.pso(n_particles,
n_iterations,
lower_start=None,
upper_start=None,
threadCount=1,
init_pos=None,
mpi=False,
print_key='PSO')
assert len(result) == 16
def test_mcmc_emcee(self):
n_walkers = 36
n_run = 2
n_burn = 2
mean_start = self.param_class.setParams(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
sigma_start = np.ones_like(mean_start) * 0.1
samples = self.sampler.mcmc_emcee(n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=False)
assert len(samples) == n_walkers * n_run
def test_mcmc_CH(self):
walkerRatio = 2
n_run = 2
n_burn = 2
mean_start = self.param_class.setParams(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
sigma_start = np.ones_like(mean_start) * 0.1
self.sampler.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
sigma_start,
threadCount=1,
init_pos=None,
mpi=False)
def mcmc(
self,
n_burn,
n_run,
walkerRatio=None,
n_walkers=None,
sigma_scale=1,
threadCount=1,
init_samples=None,
re_use_samples=True,
sampler_type='EMCEE',
progress=True,
backend_filename=None,
start_from_backend=False,
# zeus specific kwargs
moves=None,
tune=True,
tolerance=0.05,
patience=5,
maxsteps=10000,
mu=1.0,
maxiter=10000,
pool=None,
vectorize=False,
blobs_dtype=None,
verbose=True,
check_walkers=True,
shuffle_ensemble=True,
light_mode=False):
"""
MCMC routine
:param n_burn: number of burn in iterations (will not be saved)
:param n_run: number of MCMC iterations that are saved
:param walkerRatio: ratio of walkers/number of free parameters
:param n_walkers: integer, number of walkers of emcee (optional, if set, overwrites the walkerRatio input
:param sigma_scale: scaling of the initial parameter spread relative to the width in the initial settings
:param threadCount: number of CPU threads. If MPI option is set, threadCount=1
:param init_samples: initial sample from where to start the MCMC process
:param re_use_samples: bool, if True, re-uses the samples described in init_samples.nOtherwise starts from
scratch.
:param sampler_type: string, which MCMC sampler to be used. Options are: 'EMCEE', 'ZEUS'
:param progress: boolean, if True shows progress bar in EMCEE
:param backend_filename: name of the HDF5 file where sampling state is saved (through emcee backend engine)
:type backend_filename: string
:param start_from_backend: if True, start from the state saved in `backup_filename`.
Otherwise, create a new backup file with name `backup_filename` (any already existing file is overwritten!).
:type start_from_backend: bool
:return: list of output arguments, e.g. MCMC samples, parameter names, logL distances of all samples specified
by the specific sampler used
"""
param_class = self.param_class
# run PSO
mcmc_class = Sampler(likelihoodModule=self.likelihoodModule)
kwargs_temp = self._updateManager.parameter_state
mean_start = param_class.kwargs2args(**kwargs_temp)
kwargs_sigma = self._updateManager.sigma_kwargs
sigma_start = np.array(
param_class.kwargs2args(**kwargs_sigma)) * sigma_scale
num_param, param_list = param_class.num_param()
if n_walkers is None:
if walkerRatio is None:
raise ValueError(
'MCMC sampler needs either n_walkers or walkerRatio as input argument'
)
n_walkers = num_param * walkerRatio
# run MCMC
if init_samples is not None and re_use_samples is True:
num_samples, num_param_prev = np.shape(init_samples)
if num_param_prev == num_param:
print(
"re-using previous samples to initialize the next MCMC run."
)
idxs = np.random.choice(len(init_samples), n_walkers)
initpos = init_samples[idxs]
else:
raise ValueError(
"Can not re-use previous MCMC samples as number of parameters have changed!"
)
else:
initpos = None
if sampler_type == 'EMCEE':
samples, dist = mcmc_class.mcmc_emcee(
n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=self._mpi,
threadCount=threadCount,
progress=progress,
initpos=initpos,
backend_filename=backend_filename,
start_from_backend=start_from_backend)
output = [sampler_type, samples, param_list, dist]
elif sampler_type == 'ZEUS':
samples, dist = mcmc_class.mcmc_zeus(
n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=self._mpi,
threadCount=threadCount,
progress=progress,
initpos=initpos,
backend_filename=backend_filename,
moves=moves,
tune=tune,
tolerance=tolerance,
patience=patience,
maxsteps=maxsteps,
mu=mu,
maxiter=maxiter,
pool=pool,
vectorize=vectorize,
blobs_dtype=blobs_dtype,
verbose=verbose,
check_walkers=check_walkers,
shuffle_ensemble=shuffle_ensemble,
light_mode=light_mode)
output = [sampler_type, samples, param_list, dist]
else:
raise ValueError('sampler_type %s not supported!' % sampler_type)
self._mcmc_init_samples = samples # overwrites previous samples to continue from there in the next MCMC run
return output
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': True,
'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)
self.sampler = Sampler(likelihoodModule=self.Likelihood)
class TestFittingSequence(object):
"""
test the fitting sequences
"""
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': True,
'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)
self.sampler = Sampler(likelihoodModule=self.Likelihood)
def test_pso(self):
n_particles = 2
n_iterations = 2
result, chain = self.sampler.pso(n_particles,
n_iterations,
lower_start=None,
upper_start=None,
threadCount=1,
init_pos=None,
mpi=False,
print_key='PSO')
assert len(result) == 16
def test_mcmc_emcee(self):
n_walkers = 36
n_run = 2
n_burn = 2
mean_start = self.param_class.kwargs2args(
kwargs_lens=self.kwargs_lens,
kwargs_source=self.kwargs_source,
kwargs_lens_light=self.kwargs_lens_light)
sigma_start = np.ones_like(mean_start) * 0.1
samples, dist = self.sampler.mcmc_emcee(n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=False)
assert len(samples) == n_walkers * n_run
def mcmc(self,
n_burn,
n_run,
walkerRatio,
sigma_scale=1,
threadCount=1,
init_samples=None,
re_use_samples=True,
sampler_type='COSMOHAMMER'):
"""
MCMC routine
:param n_burn: number of burn in iterations (will not be saved)
:param n_run: number of MCMC iterations that are saved
:param walkerRatio: ratio of walkers/number of free parameters
:param sigma_scale: scaling of the initial parameter spread relative to the width in the initial settings
:param threadCount: number of CPU threads. If MPI option is set, threadCount=1
:param init_samples: initial sample from where to start the MCMC process
:param re_use_samples: bool, if True, re-uses the samples described in init_samples.nOtherwise starts from scratch.
:param sampler_type: string, which MCMC sampler to be used. Options are: 'COSMOHAMMER, and 'EMCEE'
:return: list of output arguments, e.g. MCMC samples, parameter names, logL distances of all samples specified by the specific sampler used
"""
param_class = self.param_class
# run PSO
mcmc_class = Sampler(likelihoodModule=self.likelihoodModule)
kwargs_temp = self._updateManager.parameter_state
mean_start = param_class.kwargs2args(**kwargs_temp)
kwargs_sigma = self._updateManager.sigma_kwargs
sigma_start = param_class.kwargs2args(**kwargs_sigma)
num_param, param_list = param_class.num_param()
# run MCMC
if not init_samples is None and re_use_samples is True:
print("test that you are here!")
num_samples, num_param_prev = np.shape(init_samples)
print(num_samples, num_param_prev, num_param,
'shape of init_sample')
if num_param_prev == num_param:
print(
"re-using previous samples to initialize the next MCMC run."
)
initpos = ReusePositionGenerator(init_samples)
else:
print(
"Can not re-use previous MCMC samples due to change in option"
)
initpos = None
else:
initpos = None
if sampler_type is 'COSMOHAMMER':
samples, dist = mcmc_class.mcmc_CH(walkerRatio,
n_run,
n_burn,
mean_start,
np.array(sigma_start) *
sigma_scale,
threadCount=threadCount,
mpi=self._mpi,
init_pos=initpos)
output = [sampler_type, samples, param_list, dist]
elif sampler_type is 'EMCEE':
n_walkers = num_param * walkerRatio
samples = mcmc_class.mcmc_emcee(n_walkers,
n_run,
n_burn,
mean_start,
sigma_start,
mpi=self._mpi)
output = [sampler_type, samples, param_list]
else:
raise ValueError('sampler_type %s not supported!' % sampler_type)
self._mcmc_init_samples = samples # overwrites previous samples to continue from there in the next MCMC run
return output