Strong lens modeling package.
Project description
lenstronomy is a multi-purpose package to model strong gravitational lenses. The software is based on Birrer et al 2015 and finds application in e.g. Birrer et al 2016 for time-delay cosmography and measuring the expansion rate of the universe and Birrer et al 2017 for quantifying lensing substructure to infer dark matter properties.
The development is coordinated on GitHub and contributions are welcome. The documentation of lenstronomy is available at readthedocs.org and the package is distributed over PyPI.
Installation
$ 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 (needs a fortran compiler)
Additional python libraries:
CosmoHammer (through PyPi)
astropy
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
Kinematic modelling
Cosmographic inference tools
Getting started
The starting guide jupyter notebook 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. You can find simple examle notebooks for various cases.
Shapelet reconstruction demonstration movies
We provide some examples where a real galaxy has been lensed and then been reconstructed by a shapelet basis set.
Attribution
The design concept of lenstronomy are reported in Birrer & Amara 2018. Please cite this paper whenever you publish results that made use of lenstronomy. Please also cite Birrer et al 2015 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.