/SMPy

From Shear to Map: A Python-based approach to constructing convergence maps through the Kaiser-Squires inversion.

Primary LanguageJupyter NotebookMIT LicenseMIT

SMPy (Shear Mapping in Python)

Overview

SMPy is a Python-based toolkit designed for astrophysicists and cosmologists, facilitating the construction of convergence maps from shear data. This tool leverages the power of Python to provide an accessible and efficient way to analyze gravitational lensing effects, particularly focusing on the mapping of dark matter distribution in galaxy clusters.

Features

  • Efficient algorithms for shear-to-convergence mapping.
  • Support for various inversion techniques, including Kaiser-Squires.
  • User-friendly interface for data handling and visualization.
  • Compatibility with standard astrophysical data formats.

Installation

  1. Prerequisites: Ensure you have Python 3.x installed on your system. SMPy also requires numpy, scipy, pandas, astropy, and matplotlib for numerical computations and visualizations.

  2. Clone the Repository: Clone the SMPy repository to your local machine using git:

    git clone https://github.com/GeorgeVassilakis/SMPy.git

Example

K-maphttps://github.com/GeorgeVassilakis/SMPy/blob/main/notebooks/simulation_kmap.png)