Metadata-Version: 2.1
Name: autolens
Version: 1.7.0
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://github.com/Jammy2211/PyAutoLens/blob/development/imageaxis.png
        
        Installation
        ------------
        
        **PyAutoLens** requires Python 3.6+ and you can install it via ``pip`` or ``conda`` (see
        `this link <https://pyautolens.readthedocs.io/en/latest/general/installation.html#installation-with-conda>`_
        for ``conda`` instructions).
        
        .. code-block:: bash
        
            pip install autolens
        
        Next, clone the `autolens_workspace <https://github.com/Jammy2211/autolens_workspace>`_, which includes
        **PyAutoLens** configuration files, example scripts and more!
        
        .. code-block:: bash
        
           cd /path/on/your/computer/you/want/to/put/the/autolens_workspace
           git clone https://github.com/Jammy2211/autolens_workspace --depth 1
           cd autolens_workspace
        
        Finally, run ``welcome.py`` in the ``autolens_workspace`` to get started!
        
        .. code-block:: bash
        
           python3 welcome.py
        
        If your installation had an error, check the
        `troubleshooting section <https://pyautolens.readthedocs.io/en/latest/general/installation.html#trouble-shooting>`_ on
        our readthedocs.
        
        If you would prefer to Fork or Clone the **PyAutoLens** GitHub repo, checkout the
        `cloning section <https://pyautolens.readthedocs.io/en/latest/general/installation.html#forking-cloning>`_ on our
        readthedocs.
        
        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 ``EllipticalIsothermal`` ``MassProfile`` lenses a background source at redshift 1.0 with an
        ``EllipticalExponential`` ``LightProfile`` representing a disk.
        
        .. code-block:: python
        
            import autolens as al
            import autolens.plot as aplt
        
            """
            To describe the deflection of light by mass, two-dimensional grids of (y,x) Cartesian
            coordinates are used.
            """
        
            grid = al.Grid.uniform(
                shape_2d=(50, 50),
                pixel_scales=0.05,  # <- Conversion from pixel units to arc-seconds.
            )
        
            """The lens galaxy has an EllipticalIsothermal MassProfile and is at redshift 0.5."""
        
            mass = 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=mass)
        
            """The source galaxy has an EllipticalExponential LightProfile and is at redshift 1.0."""
        
            disk = 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, disk=disk)
        
            """
            We create the strong lens using a Tracer, which uses the galaxies, 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 Grid and 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 autolens.plot as aplt
        
            """Use the dataset path and lens name to load the imaging data."""
        
            imaging = al.Imaging.from_fits(
                image_path="/path/to/dataset/image.fits",
                noise_map_path="/path/to/dataset/noise_map.fits",
                psf_path="/path/to/dataset/psf.fits",
                pixel_scales=0.1,
            )
        
            """Create a mask for the data, which we setup as a 3.0" circle."""
        
            mask = al.Mask2D.circular(
                shape_2d=imaging.shape_2d, pixel_scales=imaging.pixel_scales, radius=3.0
            )
        
            """
            We model our lens galaxy using an EllipticalIsothermal MassProfile &
            our source galaxy as an EllipticalSersic LightProfile.
            """
        
            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, disk=source_light_profile)
        
            """
            To perform the analysis we set up a phase, which takes our galaxy models & fits
            their parameters using a `NonLinearSearch` (in this case, Dynesty).
            """
        
            phase = al.PhaseImaging(
                search=af.DynestyStatic(name="phase[example]",n_live_points=50),
                galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
            )
        
            """
            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)
            aplt.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 the installation guide, a complete overview of **PyAutoLens**'s features, examples
        scripts and tutorials, detailed API documentation and
        the `HowToLens Jupyter notebook lecture series <https://pyautolens.readthedocs.io/en/latest/howtolens/howtolens.html>`_
        on which introduces new users to strong gravitational lensing with **PyAutoLens**.
        
        Support
        -------
        
        Support for installation issues, help with lens modeling and using **PyAutoLens** is available by
        `raising an issue on the autolens_workspace GitHub page <https://github.com/Jammy2211/autolens_workspace/issues>`_. or
        joining the **PyAutoLens** `Slack channel <https://pyautolens.slack.com/>`_, where we also provide the latest updates on
        **PyAutoLens**.
        
        Slack is invitation-only, so if you'd like to join send 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
