Metadata-Version: 2.4
Name: erresire
Version: 0.1.1
Summary: Simulate large populations of strong gravitational lenses (Monte Carlo‑based).
Author-email: Lorena Mezini <lormezini@gmail.com>
License: MIT
Project-URL: Repository, https://github.com/lmezini/Erresire
Project-URL: Homepage, https://github.com/lmezini/Erresire
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: lenstronomy
Requires-Dist: shapely
Requires-Dist: colossus
Requires-Dist: scikit-learn
Requires-Dist: astropy
Dynamic: license-file

<img width="403" height="150" alt="Erresire(2)" src="https://github.com/user-attachments/assets/17e22142-ca63-483d-b113-b656f44d3934" />



# Erresire

**Erresire** enables users to simulate **large populations of strong gravitational lenses** in an efficient and flexible manner via a Monte Carlo method.  
Users can customize the simulation by supplying catalogs of their choice for dark matter halos, galaxies, and sources.

###  Installation

You can install the latest version directly from PyPI:

```bash
pip install erresire
```

### Quickstart

The fastest way to get started is with the **model_run_example.ipynb** notebook provided in the `examples/` folder.

This notebook illustrates how to use the core Erresire functions and shows how to integrate **custom lens models** into your simulations.

Also within this directory are mini catalogs of galaxy, halo, and source properties. These small datasets allow you to quickly run the example notebook and explore the functionality of Erresire without needing large external files.

Galaxy data comes from the ComsoDC2 survey and source data from the Qauia UnWISE survey. Creation of the halo data catalog is discussed in Mezini 2025 using particle data from the Symphony Simulation suite.
