Metadata-Version: 1.1
Name: lenstronomy
Version: 1.0.1
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/1.0.1.tar.gz
Description: ========================================================
        lenstronomy - gravitational lensing software package
        ========================================================
        
        .. image:: https://badge.fury.io/py/lenstronomy.png
            :target: http://badge.fury.io/py/lenstronomy
        
        .. image:: https://travis-ci.org/sibirrer/lenstronomy.png?branch=master
                :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
        
        .. image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat
            :target: https://github.com/sibirrer/lenstronomy/blob/master/LICENSE
        
        .. 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>`_ and
        `Birrer et al 2018 <http://adsabs.harvard.edu/abs/2018arXiv180901274B>`_ 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>`_ 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)
        
        Additional python libraries:
        
        * ``CosmoHammer`` (through PyPi)
        * ``astropy``
        * ``dynesty``
        * ``pymultinest``
        * ``pypolychord``
        * ``nestcheck``
        * standard python libraries (``numpy``, ``scipy``)
        
        
        
        Modelling Features
        ------------------
        
        * a variety of lens models to use in arbitrary superposition
        * lens equation solver
        * multi-plane ray-tracing
        * Extended source reconstruction with basis sets (shapelets) and analytic light profiles
        * Point sources
        * numerical options for sub-grid ray-tracing and sub-pixel convolution
        * non-linear line-of-sight description
        * iterative point spread function reconstruction
        * linear and non-linear optimization modules
        * Pre-defined plotting and illustration routines
        * Particle swarm optimization for parameter fitting
        * MCMC (emcee from CosmoHammer) for parameter inferences
        * Nested Sampling (MultiNest, DyPolyChord, or Dynesty) for evidence computation and parameter inferences
        * Kinematic modelling
        * Cosmographic inference tools
        
        
        
        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 `http://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>`_
        * `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>`_
        
        
        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 whenever you publish
        results that made use of ``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
