Metadata-Version: 2.1
Name: autolens
Version: 1.0.13
Summary: Automated Strong Gravitational Lens Modeling
Home-page: https://github.com/jammy2211/PyAutoLens
Author: James Nightingale and Richard Hayes
Author-email: james.w.nightingale@durham.ac.uk
License: MIT License
Description: PyAutoLens
        ==========
        
        When two or more galaxies are aligned perfectly down our line-of-sight, the background galaxy appears multiple times.
        This is called strong gravitational lensing, & **PyAutoLens** makes it simple to model strong gravitational lenses,
        like this one:
        
        .. image:: https://raw.githubusercontent.com/Jammy2211/PyAutoLens/master/gitimage.png
          :width: 400
          :alt: Alternative text
        
        **PyAutoLens** is based on the following papers:
        
        `Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging <https://arxiv.org/abs/1412.7436>`_
        
        `AutoLens: Automated Modeling of a Strong Lens's Light, Mass & Source <https://arxiv.org/abs/1708.07377>`_
        
        API Overview
        ------------
        
        Lensing calculations are performed in **PyAutoLens** by building a *Tracer* object from *LightProfile*, *MassProfile*
        and *Galaxy* objects. Below, we create a simple strong lens system where a redshift 0.5 lens galaxy with an Isothermal
        mass profile lenses a background source at redshift 1.0 with an Exponential light profile.
        
        .. code-block:: python
        
            import autolens as al
            import autolens.plot as aplt
        
            """
            To describe the deflection of light grids are used which are two-dimensional Cartesian grids
            of (y,x) coordinates which are deflected by mass profiles.
            """
        
            grid = al.Grid.uniform(
                shape_2d=(50, 50),
                pixel_scales=0.05,  # <- Conversion from pixel units to arc-seconds.
            )
        
            """The lens galaxy is at redshift 0.5 and its mass profile is an elliptical Isothermal."""
        
            sie = al.mp.EllipticalIsothermal(
                centre=(0.0, 0.0), elliptical_comps=(0.1, 0.05), einstein_radius=1.6
            )
        
            lens_galaxy = al.Galaxy(redshift=0.5, mass=sie)
        
            """The source galaxy is at redshift 1.0, and its light profile is elliptical Exponential."""
        
            exponential = al.lp.EllipticalExponential(
                centre=(0.3, 0.2),
                elliptical_comps=(0.05, 0.25),
                intensity=0.05,
                effective_radius=0.5,
            )
        
            source_galaxy = al.Galaxy(redshift=1.0, light=exponential)
        
            """
            We create the strong lens system by performing ray-tracing via a Tracer object, which uses the
            galaxies above, their redshifts and an input cosmology to determine how light is deflected on
            its path to Earth.
            """
        
            tracer = al.Tracer.from_galaxies(
                galaxies=[lens_galaxy, source_galaxy], cosmology=cosmo.Planck15
            )
        
            """
            We can use the tracer to perform many lensing calculations, for example plotting the
            image of the lensed source.
            """
        
            aplt.Tracer.image(tracer=tracer, grid=grid)
        
        With **PyAutoLens**, you can begin modeling a lens in just a couple of minutes. The example below demonstrates a simple
        analysis which fits the foreground lens galaxy's mass & the background source galaxy's light.
        
        .. code-block:: python
        
            import autofit as af
            import autolens as al
        
            import os
        
            """In this example, we'll fit a simple lens galaxy + source galaxy system."""
        
            dataset_path = "{}/../data".format(os.path.dirname(os.path.realpath(__file__)))
            lens_name = "example_lens"
        
            """Use the relative path to the dataset to load the imaging data."""
        
            imaging = al.Imaging.from_fits(
                image_path=f"{dataset_path}/{lens_name}/image.fits",
                noise_map_path=f"{dataset_path}/{lens_name}/noise_map.fits",
                psf_path=f"{dataset_path}/{lens_name}/psf.fits",
                pixel_scales=0.1,
            )
        
            """Create a mask for the data, which we setup as a 3.0" circle."""
        
            mask = al.Mask.circular(
                shape_2d=imaging.shape_2d, pixel_scales=imaging.pixel_scales, radius=3.0
            )
        
            """
            We model our lens galaxy using a mass profile (a singular isothermal ellipsoid) &
            our source galaxy a light profile (an elliptical Sersic).
            """
        
            lens_mass_profile = al.mp.EllipticalIsothermal
            source_light_profile = al.lp.EllipticalSersic
        
            """
            To setup our model galaxies, we use the GalaxyModel class, which represents a
            galaxy whose parameters are free & fitted for by PyAutoLens.
            """
        
            lens_galaxy_model = al.GalaxyModel(redshift=0.5, mass=lens_mass_profile)
            source_galaxy_model = al.GalaxyModel(redshift=1.0, light=source_light_profile)
        
            """
            To perform the analysis we set up a phase, which takes our galaxy models & fits
            their parameters using a non-linear search (in this case, Dynesty).
            """
        
            phase = al.PhaseImaging(
                galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
                phase_name="example/phase_example",
                search=af.DynestyStatic(n_live_points=50, sampling_efficiency=0.5),
            )
        
            """
            We pass the imaging data and mask to the phase, thereby fitting it with the lens
            model & plot the resulting fit.
            """
        
            result = phase.run(dataset=imaging, mask=mask)
            al.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)
        
        Getting Started
        ---------------
        
        To get started checkout our `readthedocs <https://pyautolens.readthedocs.io/>`_,
        where you'll find our installation guide, a complete overview of **PyAutoLens**'s features, examples scripts and
        tutorials and detailed API documentation.
        
        Slack
        -----
        
        We're building a **PyAutoLens** community on Slack, so you should contact us on our
        `Slack channel <https://pyautolens.slack.com/>`_ before getting started. Here, I will give you the latest updates on
        the software & discuss how best to use **PyAutoLens** for your science case.
        
        Unfortunately, Slack is invitation-only, so first send me an `email <https://github.com/Jammy2211>`_ requesting an
        invite.
        
Keywords: cli
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: test
