__author__ = 'sibirrer'
import time
import copy
from lenstronomy.Sampling.Pool.pool import choose_pool
from lenstronomy.ImSim.MultiBand.single_band_multi_model import SingleBandMultiModel
from lenstronomy.Sampling.Samplers.pso import ParticleSwarmOptimizer
__all__ = ['AlignmentFitting', 'AlignmentLikelihood']
[docs]class AlignmentFitting(object):
"""
class which executes the different sampling methods
"""
def __init__(self, multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None):
"""
initialise the classes of the chain and for parameter options
"""
self.chain = AlignmentLikelihood(multi_band_list, kwargs_model, kwargs_params, band_index, likelihood_mask_list)
[docs] def pso(self, n_particles=10, n_iterations=10, lowerLimit=-0.2, upperLimit=0.2, threadCount=1, mpi=False,
print_key='default'):
"""
returns the best fit for the lense model on catalogue basis with particle swarm optimizer
"""
init_pos = self.chain.get_args(self.chain.kwargs_data_init)
num_param = self.chain.num_param
lowerLimit = [lowerLimit] * num_param
upperLimit = [upperLimit] * num_param
pool = choose_pool(mpi=mpi, processes=threadCount, use_dill=True)
pso = ParticleSwarmOptimizer(self.chain, lowerLimit, upperLimit,
n_particles, pool=pool)
if init_pos is not None:
pso.set_global_best(init_pos, [0]*len(init_pos),
self.chain.likelihood(init_pos))
if pool.is_master():
print('Computing the %s ...' % print_key)
time_start = time.time()
result, [chi2_list, pos_list, vel_list] = pso.optimize(n_iterations)
kwargs_data = self.chain.update_data(result)
if pool.is_master():
time_end = time.time()
print("Shifts found: ", result)
print(time_end - time_start, 'time used for ', print_key)
return kwargs_data, [chi2_list, pos_list, vel_list]
[docs]class AlignmentLikelihood(object):
def __init__(self, multi_band_list, kwargs_model, kwargs_params, band_index=0, likelihood_mask_list=None):
"""
initializes all the classes needed for the chain
"""
# print('initialized on cpu', threading.current_thread())
self._multi_band_list = multi_band_list
self.kwargs_data_init = multi_band_list[band_index][0]
self._kwargs_data_shifted = copy.deepcopy(self.kwargs_data_init)
self._kwargs_model = kwargs_model
self._source_marg = False
self._band_index = band_index
self._likelihood_mask_list = likelihood_mask_list
self._kwargs_params = kwargs_params
def _likelihood(self, args):
"""
routine to compute X2 given variable parameters for a MCMC/PSO chainF
"""
# generate image and computes likelihood
multi_band_list = self.update_multi_band(args)
image_model = SingleBandMultiModel(multi_band_list, self._kwargs_model,
likelihood_mask_list=self._likelihood_mask_list, band_index=self._band_index)
logL = image_model.likelihood_data_given_model(source_marg=self._source_marg, **self._kwargs_params)
return logL
def __call__(self, a):
return self._likelihood(a)
[docs] def likelihood(self, a):
return self._likelihood(a)
[docs] def computeLikelihood(self, ctx):
logL, _ = self._likelihood(ctx.args2kwargs())
return logL
[docs] def update_multi_band(self, args):
"""
:param args: list of parameters
:return: updated multi_band_list
"""
kwargs_data = self.update_data(args)
multi_band_list = self._multi_band_list
multi_band_list[self._band_index][0] = kwargs_data
return multi_band_list
[docs] def update_data(self, args):
"""
:param args:
:return:
"""
k = 0
kwargs_data = self._kwargs_data_shifted
kwargs_data['ra_shift'] = args[k]
kwargs_data['dec_shift'] = args[k+1]
k += 2
return kwargs_data
[docs] @staticmethod
def get_args(kwargs_data):
"""
:param kwargs_data:
:return:
"""
args = []
args.append(kwargs_data.get('ra_shift', 0))
args.append(kwargs_data.get('dec_shift', 0))
return args
@property
def num_param(self):
n = 2
return n