Metadata-Version: 1.1
Name: lenstronomy
Version: 1.4.0
Summary: Strong lens modeling package.
Home-page: https://github.com/sibirrer/lenstronomy
Author: Simon Birrer
Author-email: sibirrer@gmail.com
License: MIT
Download-URL: https://github.com/sibirrer/lenstronomy/archive/1.4.0.tar.gz
Description: ========================================================
        lenstronomy - gravitational lensing software package
        ========================================================
        
        .. image:: docs/figures/readme_fig.png
        
        .. image:: https://badge.fury.io/py/lenstronomy.png
            :target: http://badge.fury.io/py/lenstronomy
        
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                :target: https://travis-ci.org/sibirrer/lenstronomy
        
        .. image:: https://readthedocs.org/projects/lenstronomy/badge/?version=latest
                :target: http://lenstronomy.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        .. image:: https://coveralls.io/repos/github/sibirrer/lenstronomy/badge.svg?branch=master
                :target: https://coveralls.io/github/sibirrer/lenstronomy?branch=master
        
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        .. image:: https://img.shields.io/badge/arXiv-1803.09746%20-yellowgreen.svg
            :target: https://arxiv.org/abs/1803.09746
        
        ``lenstronomy`` is a multi-purpose package to model strong gravitational lenses. The software package is presented in
        `Birrer & Amara 2018 <https://arxiv.org/abs/1803.09746v1>`_ and is based on `Birrer et al 2015 <http://adsabs.harvard.edu/abs/2015ApJ...813..102B>`_.
        ``lenstronomy`` finds application in e.g. `Birrer et al 2016 <http://adsabs.harvard.edu/abs/2016JCAP...08..020B>`_,
        `Birrer et al 2018 <http://adsabs.harvard.edu/abs/2018arXiv180901274B>`_ and `Shajib et al 2019 <https://arxiv.org/abs/1910.06306>`_ for time-delay cosmography and measuring
        the expansion rate of the universe and `Birrer et al 2017 <http://adsabs.harvard.edu/abs/2017JCAP...05..037B>`_ and `Gilman et al. 2019 <https://ui.adsabs.harvard.edu/abs/2019arXiv190806983G/abstract>`_ for
        quantifying lensing substructure to infer dark matter properties.
        
        
        The development is coordinated on `GitHub <https://github.com/sibirrer/lenstronomy>`_ and contributions are welcome.
        The documentation of ``lenstronomy`` is available at `readthedocs.org <http://lenstronomy.readthedocs.org/>`_ and
        the package is distributed over `PyPI <https://pypi.python.org/pypi/lenstronomy>`_.
        
        
        
        Installation
        ------------
        
        .. code-block:: bash
        
            $ pip install lenstronomy --user
        
        
        Requirements
        ------------
        To run lens models with elliptical mass distributions, the fastell4py package, originally from Barkana (fastell),
        is also required and can be cloned from: `https://github.com/sibirrer/fastell4py <https://github.com/sibirrer/fastell4py>`_ (needs a fortran compiler)
        
        .. code-block:: bash
        
            $ sudo apt-get install gfortran
            $ git clone https://github.com/sibirrer/fastell4py.git <desired location>
            $ cd <desired location>
            $ python setup.py install --user
        
        
        Additional python libraries are e.g. : ``numpy``, ``scipy``, ``matplotlib`` ``astropy``, ``dynesty``, ``pymultinest``, ``pypolychord``, ``nestcheck``, ``CosmoHammer``
        
        
        
        Modelling Features
        ------------------
        
        * a variety of analytic lens model profiles
        * various lensing computation tools (lens equation solver, ray-tracing etc)
        * integrated support for multi-lens plane and multi-source plane modelling
        * API to conveniently simulate mock lenses
        * Extended source reconstruction with basis sets (shapelets)
        * numerical options for sub-grid ray-tracing and sub-pixel convolution
        * Particle swarm optimization for parameter fitting with MPI and multi-threading support
        * MCMC (emcee) and nested sampling (MultiNest, DyPolyChord, or Dynesty) with MPI and multi-threading support
        * Kinematic modelling (Jeans anisotropy models) of lens deflector galaxy
        * Cosmographic inference tools
        * ...and much more
        
        
        
        Getting started
        ---------------
        
        The `starting guide jupyter notebook <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/starting_guide.ipynb>`_
        leads through the main modules and design features of ``lenstronomy``. The modular design of ``lenstronomy`` allows the
        user to directly access a lot of tools and each module can also be used as stand-alone packages.
        
        
        Example notebooks
        -----------------
        
        We have made an extension module available at `https://github.com/sibirrer/lenstronomy_extensions <https://github.com/sibirrer/lenstronomy_extensions>`_.
        You can find simple examle notebooks for various cases. The latest versions of the notebooks should be compatible with the recent pip version of lenstronomy.
        
        * `Units, coordiante system and parameter definitions in lenstronomy <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/units_coordinates_parameters.ipynb>`_
        * `FITS handling and extracting needed information from the data prior to modeling <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/fits_handling.ipynb>`_
        * `Modeling a simple Einstein ring <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/simple_ring.ipynb>`_
        * `Quadrupoly lensed quasar modelling <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/quad_model.ipynb>`_
        * `Double lensed quasar modelling <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/double_model.ipynb>`_
        * `Time-delay cosmography <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/time-delay%20cosmography.ipynb>`_
        * `Source reconstruction and deconvolution with Shapelets <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/shapelet_source_modelling.ipynb>`_
        * `Solving the lens equation <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/lens_equation.ipynb>`_
        * `Measuring cosmic shear with Einstein rings <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/EinsteinRingShear_simulations.ipynb>`_
        * `Fitting of galaxy light profiles, like e.g. GALFIT <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/galfitting.ipynb>`_
        * `Quasar-host galaxy decomposition <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/quasar-host%20decomposition.ipynb>`_
        * `Hiding and seeking a single subclump <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/substructure_challenge_simple.ipynb>`_
        * `Mock generation of realistic images with substructure in the lens <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/substructure_challenge_mock_production.ipynb>`_
        * `Mock simulation API with multi color models <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/simulation_api.ipynb>`_
        * `Catalogue data modeling of image positions, flux ratios and time delays <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/catalogue%20modelling.ipynb>`_
        * `Example of numerical ray-tracing and convolution options <https://github.com/sibirrer/lenstronomy_extensions/blob/master/lenstronomy_extensions/Notebooks/lenstronomy_numerics.ipynb>`_
        
        
        
        Contribution
        ------------
        Check out the contributing page `contributing page <https://lenstronomy.readthedocs.io/en/latest/contributing.html>`_
        and become an author of lenstronomy! A big shutout to the current `list of contributors and developers <https://lenstronomy.readthedocs.io/en/latest/authors.html>`_!
        
        
        
        Affiliated packages
        -------------------
        Multiple affiliated packages that make use of lenstronomy can be found `here <https://lenstronomy.readthedocs.io/en/latest/affiliatedpackages.html>`_
        (not complete) and further packages are under development by the community.
        
        
        Mailing list
        ------------
        
        You can join the **lenstronomy** mailing list by signing up on the
        `google groups page <https://groups.google.com/forum/#!forum/lenstronomy>`_.
        
        
        The email list is meant to provide a communication platform between users and developers. You can ask questions,
        and suggest new features. New releases will be announced via this mailing list.
        
        If you encounter errors or problems with **lenstronomy**, please let us know!
        
        
        Shapelet reconstruction demonstration movies
        --------------------------------------------
        
        We provide some examples where a real galaxy has been lensed and then been reconstructed by a shapelet basis set.
        
        * `HST quality data with perfect knowledge of the lens model <http://www.astro.ucla.edu/~sibirrer/video/true_reconstruct.mp4>`_
        * `HST quality with a clump hidden in the data <http://www.astro.ucla.edu/~sibirrer/video/clump_reconstruct.mp4>`_
        * `Extremely large telescope quality data with a clump hidden in the data <http://www.astro.ucla.edu/~sibirrer/video/TMT_high_res_clump_reconstruct.mp4>`_
        
        
        
        Attribution
        -----------
        The design concept of ``lenstronomy`` are reported in `Birrer & Amara 2018 <https://arxiv.org/abs/1803.09746v1>`_.
        Please cite this paper when you use lenstronomy in a publication and link to `https://github.com/sibirrer/lenstronomy <https://github.com/sibirrer/lenstronomy>`_.
        Please also cite `Birrer et al 2015 <http://adsabs.harvard.edu/abs/2015ApJ...813..102B>`_
        when you make use of the ``lenstronomy`` work-flow or the Shapelet source reconstruction. Please make sure to cite also
        the relevant work that was implemented in ``lenstronomy``, as described in the release paper.
        
Keywords: lenstronomy
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.6
