Metadata-Version: 2.1
Name: autolens
Version: 0.43.2
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>`_
        
        Example
        -------
        
        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'
        
            # Get the relative path to the data in our workspace & load the imaging data.
            imaging = al.Imaging.from_fits(
                image_path=dataset_path + lens_name + '/image.fits',
                psf_path=dataset_path+lens_name+'/psf.fits',
                noise_map_path=dataset_path+lens_name+'/noise_map.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 model & fitted for by PyAutoLens. The galaxies are also assigned redshifts.
            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, MultiNest).
            phase = al.PhaseImaging(
                galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
                phase_name='example/phase_example', non_linear_class=af.MultiNest)
        
            # We pass the imaging data and mask to the phase, thereby fitting it with the lens model above & plot the resulting fit.
            result = phase.run(data=imaging, mask=mask)
            al.plot.FitImaging.subplot_fit_imaging(fit=result.most_likely_fit)
        
        Features
        --------
        
        **PyAutoLens's** advanced modeling features include:
        
        - **Galaxies** - Use light & mass profiles to make galaxies & perform lensing calculations.
        - **Pipelines** - Write automated analysis pipelines to fit complex lens models to large samples of strong lenses.
        - **Extended Sources** - Reconstruct complex source galaxy morphologies on a variety of pixel-grids.
        - **Adaption** - Adapt the lensing analysis to the features of the observed strong lens imaging.
        - **Multi-Plane** - Perform multi-plane ray-tracing & model multi-plane lens systems.
        - **Visualization** - Custom visualization libraries for plotting physical lensing quantities & modeling results.
        
        HowToLens
        ---------
        
        Included with **PyAutoLens** is the **HowToLens** lecture series, which provides an introduction to strong gravitational lens modeling with **PyAutoLens**. It can be found in the workspace & consists of 5 chapters:
        
        - **Introduction** - An introduction to strong gravitational lensing & **PyAutolens**.
        - **Lens Modeling** - How to model strong lenses, including a primer on Bayesian non-linear analysis.
        - **Pipelines** - How to build pipelines & tailor them to your own science case.
        - **Inversions** - How to perform pixelized reconstructions of the source-galaxy.
        - **Hyper-Mode** - How to use **PyAutoLens** advanced modeling features that adapt the model to the strong lens being analysed.
        
        Workspace
        ---------
        
        **PyAutoLens** comes with a workspace, which can be found `here <https://github.com/Jammy2211/autolens_workspace>`_ & which includes:
        
        - **Aggregator** - Manipulate large suites of modeling results via Jupyter notebooks, using **PyAutoFit**'s in-built results database.
        - **API** - Illustrative scripts of the **PyAutoLens** interface, for examples on how to make plots, peform lensing calculations, etc.
        - **Config** - Configuration files which customize **PyAutoLens**'s behaviour.
        - **Dataset** - Where data is stored, including example datasets distributed with **PyAutoLens**.
        - **HowToLens** - The **HowToLens** lecture series.
        - **Output** - Where the **PyAutoLens** analysis and visualization are output.
        - **Pipelines** - Example pipelines for modeling strong lenses.
        - **Preprocess** - Tools to preprocess data before an analysis (e.g. convert units, create masks).
        - **Quick Start** - A quick start guide, so you can begin modeling your lenses within hours.
        - **Runners** - Scripts for running a **PyAutoLens** pipeline.
        - **Simulators** - Scripts for simulating strong lens datasets with **PyAutoLens**.
        
        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.
        
        Documentation & Installation
        ----------------------------
        
        The PyAutoLens documentation can be found at our `readthedocs  <https://pyautolens.readthedocs.io/en/master>`_, including instructions on `installation <https://pyautolens.readthedocs.io/en/master/installation.html>`_.
        
        Contributing
        ------------
        
        If you have any suggestions or would like to contribute please get in touch.
        
        Papers
        ------
        
        A list of published articles using **PyAutoLens** can be found `here <https://pyautolens.readthedocs.io/en/master/papers.html>`_ .
        
        Credits
        -------
        
        **Developers**:
        
        `James Nightingale <https://github.com/Jammy2211>`_ - Lead developer & PyAutoLens guru.
        
        `Richard Hayes <https://github.com/rhayes777>`_ - Lead developer & `PyAutoFit <https://github.com/rhayes777/PyAutoFit>`_ guru.
        
        `Ashley Kelly <https://github.com/AshKelly>`_ - Developer of `pyquad <https://github.com/AshKelly/pyquad>`_ for fast deflections computations.
        
        `Amy Etherington <https://github.com/amyetherington>`_ - Magnification, Critical Curves and Caustic Calculations.
        
        `Xiaoyue Cao <https://github.com/caoxiaoyue>`_ - Analytic Ellipitcal Power-Law Deflection Angle Calculations.
        
        Qiuhan He  - NFW Profile Lensing Calculations.
        
        `Nan Li <https://github.com/linan7788626>`_ - Docker integration & support.
        
        **Code Donors**:
        
        `Andrew Robertson <https://github.com/Andrew-Robertson>`_ - Critical curve & caustic calculations.
        
        Mattia Negrello - Visibility models in the uv-plane via direct Fourier transforms.
        
        `Andrea Enia <https://github.com/AndreaEnia>`_ - Voronoi source-plane plotting tools.
        
        `Aristeidis Amvrosiadis <https://github.com/Sketos>`_ - ALMA imaging data loading.
        
        Conor O'Riordan  - Broken Power-Law mass profile.
        
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
