Dude about how to translate the results from pyautolens to lenstronomy for basic modeling. about pyautolens HOT 4 CLOSED

tracer = result.max_log_likelihood_tracer
fit = al.FitPointDict(point_dict=point_dict, tracer=tracer, positions_solver=solver)
fit["point_0"].positions.model_positions

angle=30# degrees positive from x axis
e1,e2 = param_util.phi_q2_ellipticity((angle)*np.pi/180.,q=0.7)
kwargs_lens=[{'theta_E': 0.8, 'e1': e1, 'e2': e2, 'center_x': 0.5, 'center_y': 0.5, 'gamma': 2.0}]
lensModel=LensModel(lens_model_list=['PEMD'])#SIE whit gamma variable.
source_x,source_y=0.3,0.3
numPix = 500  #  cutout pixel size
deltaPix = 0.05  #  pixel size in arcsec (area per pixel = deltaPix**2)
lens_model_class = LensModel(lens_model_list=['PEMD'], z_lens=0.5, z_source=2, cosmo=Cosmo)
lensEquationSolver = LensEquationSolver(lens_model_class)
x_image, y_image = lensEquationSolver.findBrightImage(source_x, source_y, kwargs_lens,
                                                min_distance=deltaPix, search_window=numPix * deltaPix)

[
    {
        "name": "point_0",
        "positions": [
            [
                -0.43449932,
                0.03349649
            ],
            [
                 0.79154548,
                    0.86431612
           
            ]
        ],
        "positions_noise_map": [
            0.003,
            0.003
        ],
        "fluxes": [
                21.83,
                19.39
        ],
        "fluxes_noise_map": [
            1.0,
            1.0
        ]
    }
]

point_0=al.ps.PointFlux
point_dict = al.PointDict.from_json(file_path=path.join("someplace","exp1.json"))
pixel_scale_precision=0.025       
source = af.Model(al.Galaxy, redshift=2, point_0=point_0)
model = af.Collection(galaxies=af.Collection(lens_0=af.Model(al.Galaxy, redshift=0.5, mass=al.mp.EllIsothermal),source=source))
model.galaxies.lens_0.mass.centre_0=0.5
model.galaxies.lens_0.mass.centre_1=0.5
samples = result.samples
mp_vector = samples.median_pdf_vector
grid = al.Grid2D.uniform(shape_native=(200,200), pixel_scales=(0.05, 0.05))
positions_solver = al.PositionsSolver(grid=grid, pixel_scale_precision=pixel_scale_precision)
path_prefix="/mnt/c/Users/jataq/Desktop"
dataset_name="ajustes"
search = af.DynestyStatic(path_prefix=path_prefix, name="fv2", unique_tag=dataset_name,
nlive=406,
number_of_cores=4)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=positions_solver)
result = search.fit(model=model, analysis=analysis)

tracer = al.Tracer.from_galaxies(galaxies=result.max_log_likelihood_tracer.galaxies)
solver = al.PositionsSolver(grid=grid,pixel_scale_precision=pixel_scale_precision)
fit = al.FitPointDict(point_dict=point_dict, tracer=tracer, positions_solver=solver)

[[-0.4375  0.0375]
 [ 0.7875  0.8625]]

EllIsothermal
centre: (0.5, 0.5)
elliptical_comps: (-0.07790185612420741, 0.16393196492822135)
axis_ratio: 0.6927628904038955
angle: -12.70873115953642
einstein_radius: 0.7428787155501909
slope: 2.0
core_radius: 0.0
id: 9601
_is_frozen: False
_frozen_cache: {}
_assertions: [], Redshift: 2]

e1,e2 = param_util.phi_q2_ellipticity((-12.70873115953642)*np.pi/180.,q=0.6927628904038955)#
kwargs_lens=[{'theta_E': 0.7428787155501909, 'e1': e1, 'e2': e2, 'center_x': 0.5, 'center_y': 0.5, 'gamma': 2.0}]
lensModel=LensModel(lens_model_list=['PEMD'])
source_x,source_y=mp_vector[1],mp_vector[0]
numPix = 500  #  cutout pixel size
deltaPix = 0.05  #  pixel size in arcsec (area per pixel = deltaPix**2)
lens_model_class = LensModel(lens_model_list=['PEMD'], z_lens=0.5, z_source=2, cosmo=Cosmo) #primer cambio
#lensModel = LensModel(lens_model_list=lens_model_list)
#lensModelExtensions = LensModelExtensions(lensModel=lensModel)
lensEquationSolver = LensEquationSolver(lens_model_class)
x_image, y_image = lensEquationSolver.findBrightImage(source_x, source_y, kwargs_lens,
                                                min_distance=deltaPix, search_window=numPix * deltaPix)