pySPT - Source Position Transformation¶
pySPT is a package dedicated to the Source Position
Transformation (SPT). The main goal of pySPT is to provide
a tool to quantify the systematic errors that are introduced by
the SPT in lens modeling. pySPT is free, open source software
compatible with Python 2.7 and distributed under the terms of
the MIT license.
Introduction¶
The modern time-delay cosmography aims to infer the cosmological parameters with a competitive precision from observing a multiply imaged quasar. The success of this technique relies upon a robust modeling of the lens mass distribution. Unfortunately strong degeneracies between density profiles that lead to almost the same lensing observables may bias precise estimate for the Hubble constant. The SPT, which covers the well-known mass sheet transformation (MST) as a special case, defines a new framework to investigate these degeneracies.
Recently, a detailed analysis of how the SPT may affect the
time-delay cosmography has been presented in Wertz, Orthen & Schneider (2017).
To address this question, we started by developing
a flexible numerical framework that encompasses well-tested and
efficient implementations of most of the analytical results published
in Schneider & Sluse (2014)
and Unruh, Schneider & Sluse (2017).
Numerous additional features were then added, giving rise to pySPT .
The main repository of pySPT resides on Github,
keeping an effective collaboration with Git as a version control system.
The code is under continuous development and feedback/suggestions/improvements
would be greatly appreciated. To do so, feel free to create an issue
to report a bug or send a pull request
to submit your contribution.
Documentation¶
The full documentation for pySPT can be found here: http://pyspt.readthedocs.io.
Several tutorials in the form of Jupyter notebooks can be found here: https://github.com/owertz/pySPT_tutorials
Installation and dependencies¶
pySPT relies on packages included in the python standard library
and the proven open-source libraries numpy, scipy, matplotlib
and numdifftools.
Using the setup.py file¶
First, you have to download pySPT from its Github repository as a
zip file and unzip it on your computer. Enter the pySPT root folder
where the setup.py file is located and use the command:
$ python setup.py install
Check the installation¶
To check that the installation run smoothly, open Python and use the command:
import pySPT
If everything went as planned, you will see a friendly welcome message.
Attribution¶
Most of pySPT capabilities and key features are reported in
Wertz & Orthen 2017. Please cite that paper whenever you publish
results that made use of pySPT.
