Metadata-Version: 2.2
Name: hierarc
Version: 1.2.0
Summary: Hierarchical analysis of strong lensing systems to infer lens properties and cosmological parameters simultaneously
Home-page: https://github.com/sibirrer/hierarc
Author: Simon Birrer
Author-email: sibirrer@gmail.com
License: BSD license
Keywords: hierarc
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: numpy>=1.13
Requires-Dist: scipy>=0.14.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

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hierArc
=======


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Hierarchical analysis of strong lensing systems to infer lens properties and cosmological parameters simultaneously.

The software is originated from `Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>`_ and is in active development.

* Free software: BSD license
* Documentation: https://hierarc.readthedocs.io.


Features
--------

The software allows to fit lenses with measured time delays, imaging information, kinematics constraints and
standardizable magnifications with parameters described on the ensemble level.

Installation
------------

.. code-block:: bash

    $ pip install hierarc --user


Usage
-----

The full analysis of `Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>`_ is publicly available `at this TDCOSMO repository <https://github.com/TDCOSMO/hierarchy_analysis_2020_public>`_ .
A forecast based on hierArc is presented by `Birrer & Treu 2020 <https://arxiv.org/abs/2008.06157>`_
and the notebooks are available `at this repository <https://github.com/sibirrer/TDCOSMO_forecast>`_.
The extension to using hierArc with standardizable magnifications is presented by `Birrer et al. 2021 <https://arxiv.org/abs/2107.12385>`_
and the forecast analysis is publicly available `here <https://github.com/sibirrer/glSNe>`_.
For example use cases we refer to the notebooks of these analyses.



Credits
-------

Simon Birrer & the `TDCOSMO <http://tdcosmo.org>`_ team.

Please cite `Birrer et al. 2020 <https://arxiv.org/abs/2007.02941>`_ if you make use of this software for your research.
