import autofit as af
from autolens.model.galaxy import galaxy_model as gm
from autolens.pipeline.phase import phase_imaging, phase_extensions
from autolens.pipeline import pipeline
from autolens.pipeline import tagging as tag
from autolens.model.profiles import light_profiles as lp
from autolens.model.profiles import mass_profiles as mp
from autolens.model.inversion import pixelizations as pix
from autolens.model.inversion import regularization as reg

import os

# In this pipeline, we'll perform an analysis which fits an image with the lens light included, and a source galaxy
# using an inversion, using a power-law mass profile. The pipeline follows on from the inversion pipeline
# ''pipelines/with_lens_light/inversion/from_initialize/lens_sersic_sie_shear_source_inversion.py'.

# The pipeline is two phases, as follows:

# Phase 1:

# Description: Fits the lens light and mass model as a power-law, using an inversion for the Source.
# Lens Light: EllipticalSersic
# Lens Mass: EllipitcalPowerLaw + ExternalShear
# Source Light: VoronoiMagnification + Constant
# Previous Pipelines: with_lens_light/inversion/from_initialize/lens_sersic_sie_shear_source_inversion.py
# Prior Passing: Lens Light (variable -> previous pipeline), Lens Mass (variable -> previous pipeline),
#                Source Inversion (variable / constant -> previous pipeline)
# Notes: Uses an interpolation pixel scale for fast power-law deflection angle calculations by default.

# Phase 2:

# Description: Refines the inversion parameters, using a fixed mass model from phase 1.
# Lens Light: EllipticalSersic
# Lens Mass: EllipitcalPowerLaw + ExternalShear
# Source Light: VoronoiMagnification + Constant
# Previous Pipelines: None
# Prior Passing: Lens Mass (constant -> phase 1), source inversion (variable -> phase 1)
# Notes: Uses an interpolation pixel scale for fast power-law deflection angle calculations by default.

def make_pipeline(
        pl_hyper_galaxies=True, pl_pixelization=pix.VoronoiBrightnessImage, pl_regularization=reg.AdaptiveBrightness,
        phase_folders=None, tag_phases=True,
        redshift_lens=0.5, redshift_source=1.0,
        sub_grid_size=2, bin_up_factor=None, positions_threshold=None, inner_mask_radii=None, interp_pixel_scale=0.05,
        inversion_pixel_limit=None, cluster_pixel_scale=0.1):

    ### SETUP PIPELINE AND PHASE NAMES, TAGS AND PATHS ###

    # We setup the pipeline name using the tagging module. In this case, the pipeline name is not given a tag and
    # will be the string specified below However, its good practise to use the 'tag.' function below, incase
    # a pipeline does use customized tag names.

    pipeline_name = 'pipeline_pl__lens_sersic_pl_shear_source_inversion'

    pipeline_name = tag.pipeline_name_from_name_and_settings(
        pipeline_name=pipeline_name, pixelization=pl_pixelization, regularization=pl_regularization)

    phase_folders.append(pipeline_name)

    ### PHASE 1 ###

    # In phase 1, we will fit the lens galaxy's light and mass and one source galaxy, where we:

    # 1) Pass priors on the lens galaxy's light using the EllipticalSersic of the previous pipeline.
    # 2) Pass priors on the lens galaxy's  mass using the EllipticalIsothermal and ExternalShear fit of the previous
    #    pipeline.
    # 3) Pass priors on the source galaxy's inversion using the Pixelization and Regularization of the previous pipeline.

    class LensSourcePhase(phase_imaging.LensSourcePlanePhase):

        def pass_priors(self, results):

            ### Lens Light, Sersic -> Sersic ###

            self.lens_galaxies.lens.light = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable.lens_galaxies.light

            ### Lens Mass, SIE -> Powerlaw ###

            self.lens_galaxies.lens.mass.centre = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable_absolute(a=0.05).lens_galaxies.lens.mass.centre

            self.lens_galaxies.lens.mass.axis_ratio = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable.lens_galaxies.lens.mass.axis_ratio

            self.lens_galaxies.lens.mass.phi = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable.lens_galaxies.lens.mass.phi

            self.lens_galaxies.lens.mass.einstein_radius = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable_absolute(a=0.3).lens_galaxies.lens.mass.einstein_radius

            ### Lens Shear, Shear -> Shear ###

            self.lens_galaxies.lens.shear = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').\
                variable.lens_galaxies.lens.shear

            ### Source Inversion, Inv -> Inv ###

            self.source_galaxies.source = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').inversion.\
                constant.source_galaxies.source

            ## Set all hyper-galaxies if feature is turned on ##

            if pl_hyper_galaxies:
                self.lens_galaxies.lens.hyper_galaxy = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').hyper_galaxy. \
                    constant.lens_galaxies.lens.hyper_galaxy

                self.source_galaxies.source.hyper_galaxy = results.from_phase('phase_2_lens_sersic_sie_shear_source_inversion').hyper_galaxy. \
                    constant.source_galaxies.source.hyper_galaxy

    phase1 = LensSourcePhase(
        phase_name='phase_1_lens_sersic_pl_shear_source_inversion', phase_folders=phase_folders, tag_phases=tag_phases,
        lens_galaxies=dict(
            lens=gm.GalaxyModel(
                redshift=redshift_lens,
                light=lp.EllipticalSersic,
                mass=mp.EllipticalPowerLaw,
                shear=mp.ExternalShear)),
        source_galaxies=dict(
            source=gm.GalaxyModel(
                redshift=redshift_source,
                pixelization=pl_pixelization,
                regularization=pl_regularization)),
        sub_grid_size=sub_grid_size, bin_up_factor=bin_up_factor, positions_threshold=positions_threshold,
        inner_mask_radii=inner_mask_radii, interp_pixel_scale=interp_pixel_scale,
        inversion_pixel_limit=inversion_pixel_limit, cluster_pixel_scale=cluster_pixel_scale,
        optimizer_class=af.MultiNest)

    phase1.optimizer.const_efficiency_mode = True
    phase1.optimizer.n_live_points = 75
    phase1.optimizer.sampling_efficiency = 0.2

    phase1 = phase1.extend_with_hyper_and_inversion_phases(hyper_galaxy=pl_hyper_galaxies, inversion=True)

    return pipeline.PipelineImaging(pipeline_name, phase1)