Metadata-Version: 1.1
Name: lenstronomy
Version: 0.0.3
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/0.0.3.tar.gz
Description-Content-Type: UNKNOWN
Description: =============================
        lenstronomy
        =============================
        
        .. 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
        
        
        The model package for gravitational strong lens images.
        The software is based on `Birrer et al 2015 <http://adsabs.harvard.edu/abs/2015ApJ...813..102B>`__  and finds application in
        e.g. Birrer et al. 2016 for time-delay cosmography and Birrer et al. 2017 for lensing substructure analysis.
        
        
        The development is coordinated on `GitHub <http://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
        --------
        * pip install lenstronomy
        
        
        Requirements
        -------
        To run lens models with elliptical mass distributions, the fastell4py package, originally from Barkana (fastell),
        is also required and can be cloned from: `GitHub <http://github/sibirrer/fastell4py>`__ (needs a fortran compiler)
        * CosmoHammer (through PyPi)
        * standard python libraries (numpy, scipy)
        
        
        Bug reporting and contributions
        -------
        * see CONTRIBUTING.rst
        
        
        Modelling Features
        --------
        
        * Extended source reconstruction with basis sets (shapelets)
        * Analytic light profiles for lens and source as options
        * Point sources (including solving the lens equation)
        * a variety of mass models to use
        * non-linear line-of-sight description
        * iterative point spread function
        * linear and non-linear optimization modules
        
        
        
        Analysis tools
        -------
        * Standardized fitting procedures for lens modelling
        * Modular build up to design plugins by users
        * Pre-defined plotting and illustration routines
        * Particle swarm optimization for parameter fitting
        * MCMC (emcee from CosmoHammer) for parameter inferences
        * Kinematic modelling
        * Cosmographic inference tools
        
        
        
        Example notebooks
        ------
        We have made an extension module available at `GitHub <http://github.com/sibirrer/lenstronomy_extensions>`__ .
        You can find examle notebooks for various cases, such as time-delay cosmography, substructure lensing,
        line-of-sight analysis and source reconstructions.
        
        
        
        Documentation
        -------------
        
        The full documentation can be generated with Sphinx
        
        
        
        History
        -------
        
        0.0.1 (2018-01-09)
        ++++++++++++++++++
        
        * First release on PyPI.
        
        0.0.2 (2018-01-16)
        ++++++++++++++++++
        
        * Improved testing and stability
Keywords: lenstronomy
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
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
