Metadata-Version: 2.4
Name: space-dolphin
Version: 1.3.0
Summary: Automated pipeline for lens modeling based on lenstronomy
Home-page: https://github.com/ajshajib/dolphin
Author: Anowar J. Shajib
Author-email: "Anowar J. Shajib" <ajshajib@gmail.com>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/ajshajib/dolphin
Keywords: dolphin,lenstronomy
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
License-File: AUTHORS.rst
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

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    :width: 70

|logo| dolphin
==============

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    :target: https://app.codecov.io/gh/ajshajib/dolphin/tree/main
    :alt: Codecov
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.. image:: https://img.shields.io/badge/ApJ-%20992%2040-D22630
   :target: https://iopscience.iop.org/article/10.3847/1538-4357/adf95c
   :alt: Shajib et al. 2025, ApJ, 992, 40
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    :target: https://arxiv.org/abs/2503.22657
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Welcome to **dolphin**, an AI-powered automated pipeline for strong gravitational lens modeling! 

``dolphin`` leverages `lenstronomy <https://github.com/lenstronomy/lenstronomy>`_ as its core modeling engine, providing an accessible and scalable framework for studying galaxy-scale lenses.

What is Dolphin?
----------------

Strong gravitational lens modeling traditionally requires significant manual effort. ``dolphin`` changes this by providing an AI-driven approach to forward modeling, enabling researchers to process large samples of strong lenses with ease. Whether you want a fully hands-off automated pipeline or a semi-automated workflow where you can fine-tune the AI-generated configurations, ``dolphin`` gives you the flexibility and power you need.

Features
--------

- 🤖 **AI-Automated Modeling**: Streamline forward modeling for large datasets of galaxy-scale lenses.
- 🎛️ **Flexible Workflows**: Choose between fully automated runs or semi-automated modes with manual overrides.
- 🌈 **Multi-Band Support**: Easily configure and model across multiple observing bands simultaneously.
- 🌌 **Versatile Sources**: Built-in support for both **galaxy–galaxy** and **galaxy–quasar** lens systems.
- 💻 **HPC Ready**: Seamlessly sync your setup between local machines and High-Performance Computing Clusters (HPCC).
- ✅ **Tested & Reliable**: Comprehensively tested with |Codecov|.

.. |Codecov| image:: https://codecov.io/gh/ajshajib/dolphin/branch/main/graph/badge.svg?token=WZVXZS9GF1 
    :target: https://app.codecov.io/gh/ajshajib/dolphin/tree/main

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

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   :alt: PyPI - Version
   :target: https://pypi.org/project/space-dolphin/

Installing ``dolphin`` is simple. You can install the latest stable release via ``pip``:

.. code-block:: bash

    pip install space-dolphin

Alternatively, to install the latest development version directly from GitHub:

.. code-block:: bash

    git clone https://github.com/ajshajib/dolphin.git
    cd dolphin
    pip install .

For instructions on setting up your workspace and running your first model, please check out our `Quickstart guide <QUICKSTART.rst>`_.

Citation
--------

If you use ``dolphin`` in your research, please cite the main ``dolphin`` paper:

- `Shajib et al. (2025) <https://arxiv.org/abs/2503.22657>`_

Depending on the fitting recipe used, please additionally cite the following papers for the underlying modeling methodology:

- **Galaxy-Quasar Recipe:** `Shajib et al. (2019) <https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5649S/abstract>`_
- **Galaxy-Galaxy Recipe:** `Shajib et al. (2021) <https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.2380S/abstract>`_
