Source code for autolens.point.model.analysis

import autofit as af
import autogalaxy as ag

from autolens.analysis.analysis import AnalysisLensing
from autolens.analysis.visualizer import Visualizer
from autolens.point.point_dataset import PointDict
from autolens.point.fit_point.point_dict import FitPointDict
from autolens.point.model.result import ResultPoint

from autolens.point.point_solver import PointSolver
from autolens.analysis.settings import SettingsLens

from autolens import exc

    import numba

    NumbaException = numba.errors.TypingError
except ModuleNotFoundError:
    NumbaException = ValueError

[docs]class AnalysisPoint(af.Analysis, AnalysisLensing):
[docs] def __init__( self, point_dict: PointDict, solver: PointSolver, imaging=None, cosmology: ag.cosmo.LensingCosmology = ag.cosmo.Planck15(), settings_lens=SettingsLens(), ): """ The analysis performed for model-fitting a point-source dataset, for example fitting the point-sources of a multiply imaged lensed quasar or supernovae of many source galaxies of a galaxy cluster. The analysis brings together the data, model and non-linear search in the classes `log_likelihood_function`, which is called by every iteration of the non-linear search to compute a likelihood value which samples parameter space. Parameters ---------- point_dict A dictionary containing the full point source dictionary that is used for model-fitting. solver The object which is used to determine the image-plane of source-plane positions of a model (via a `Tracer`). imaging The imaging of the point-source dataset, which is not used for model-fitting but can be used for visualization. cosmology The cosmology of the ray-tracing calculation. settings_lens Settings which control how the model-fit is performed. """ super().__init__(settings_lens=settings_lens, cosmology=cosmology) AnalysisLensing.__init__( self=self, settings_lens=settings_lens, cosmology=cosmology ) self.point_dict = point_dict self.solver = solver self.imaging = imaging
def log_likelihood_function(self, instance): """ Determine the fit of the strong lens system of lens galaxies and source galaxies to the point source data. Parameters ---------- instance A model instance with attributes Returns ------- fit : Fit A fractional value indicating how well this model fit and the model masked_imaging itself """ try: fit = self.fit_positions_for(instance=instance) return fit.log_likelihood except (AttributeError, ValueError, TypeError, NumbaException) as e: raise exc.FitException from e def fit_positions_for(self, instance): tracer = self.tracer_via_instance_from(instance=instance) return FitPointDict( point_dict=self.point_dict, tracer=tracer, point_solver=self.solver ) def visualize(self, paths, instance, during_analysis): tracer = self.tracer_via_instance_from(instance=instance) visualizer = Visualizer(visualize_path=paths.image_path) def make_result( self, samples: af.PDFSamples, model: af.Collection, sigma=1.0, use_errors=True, use_widths=False, ): return ResultPoint(samples=samples, model=model, analysis=self) def save_attributes_for_aggregator(self, paths: af.DirectoryPaths): paths.save_object("dataset", self.point_dict)