import autofit as af
import autogalaxy as ag

import os

# In this example, we'll fit an image of a single galaxy .
dataset_path = '{}/../data/'.format(os.path.dirname(os.path.realpath(__file__)))

galaxy_name = 'example_galaxy'

# Use the relative path to the dataset to load the imaging data.
imaging = ag.Imaging.from_fits(
    image_path=dataset_path + galaxy_name + '/image.fits',
    psf_path=dataset_path+galaxy_name+'/psf.fits',
    noise_map_path=dataset_path+galaxy_name+'/noise_map.fits',
    pixel_scales=0.1)

# Create a mask for the data, which we setup as a 3.0" circle.
mask = ag.Mask2D.circular(shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=3.0)

# We model our galaxy using a light profile (an elliptical Sersic).
light_profile = ag.lp.EllipticalSersic

# To setup our model galaxy, we use the GalaxyModel class, which represents a galaxy whose parameters
# are free & fitted for by PyAutoGalaxy. The galaxy is also assigned a redshift.
galaxy_model = ag.GalaxyModel(redshift=1.0, light=light_profile)

# To perform the analysis we set up a phase, which takes our galaxy model & fits its parameters using a non-linear
# search (in this case, MultiNest).
phase = ag.PhaseImaging(
    galaxies=dict(galaxy=galaxy_model),
    name='example/phase_example',
    search=af.DynestyStatic()
    )

# We pass the imaging ``data`` and mask to the phase, thereby fitting it with the galaxy model & plot the resulting fit.
result = phase.run(data=imaging, mask=mask)
ag.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)