Update README.md

Author: Mad-At-Line

README.md

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+# AstroRIM
+**Physics-Informed Inversion for Strong Gravitational Lensing (Conditional RIM + differentiable forward operator)**
 
+AstroRIM is an end-to-end pipeline for **gravitational lens inversion**: recovering an **unlensed source-plane image** from a **lensed observation** using a **Recurrent Inference Machine (RIM)** conditioned on a **learned, differentiable, physics-informed forward lensing operator**.
+
+This repository contains:
+- Simulation generators (Lenstronomy-based) for synthetic lens/source pairs
+- Inference/evaluation tooling (SSIM/MSE, FITS I/O, batch evaluation, residuals)
+- Real-lens preprocessing utilities (normalization, centering, enhancement)
+- Diagnostic/analysis tooling (mass profiles, summary figures)
+- Example figures/images used in the accompanying paper/report
+
+> **Author:** Jack Walsh  
+> **Contact:** 20jwalsh@greystonescollege.ie  
+> **School:** Greystones Community College
+
+---
+
+## Table of contents
+- [Repository layout](#repository-layout)
+- [Requirements](#requirements)
+- [Installation](#installation)
+- [Quickstart](#quickstart)
+  - [1) Generate simulations](#1-generate-simulations)
+  - [2) Run inference + evaluation](#2-run-inference--evaluation)
+  - [3) Run diagnostics / mass-profile figures](#3-run-diagnostics--mass-profile-figures)
+  - [4) Preprocess real-lens FITS](#4-preprocess-real-lens-fits)
+- [FITS format expectations](#fits-format-expectations)
+- [Reproducibility notes](#reproducibility-notes)
+- [Citing](#citing)
+- [License](#license)
+- [Contributing / Issues](#contributing--issues)
+- [Acknowledgements](#acknowledgements)
+
+---
+
+---
+
+## Requirements
+
+### Core dependencies
+- Python 3.9+ (3.10+ recommended)
+- PyTorch (CUDA optional but recommended)
+- NumPy, SciPy, Matplotlib
+- Astropy (FITS + cosmology utilities)
+- scikit-image (metrics + preprocessing)
+- Lenstronomy (simulation / lens modeling)
+
+### Optional (used by some analysis tooling)
+- tqdm (progress bars)
+- pandas (CSV export / tables)
+- h5py (HDF5 export)
+- pyyaml (YAML config support)
+- joblib (caching)
+- scikit-learn (some utilities)
+
+---
+
+## Installation
+
+### Option A — pip + venv (simple)
+```bash
+python -m venv .venv
+
+# macOS/Linux:
+source .venv/bin/activate
+
+# Windows:
+# .venv\Scripts\activate
+
+python -m pip install --upgrade pip
+
+# Core packages
+pip install numpy scipy matplotlib astropy scikit-image lenstronomy
+
+# Install PyTorch (choose the correct command for your OS/CUDA)
+# https://pytorch.org/get-started/locally/
+
+# Optional extras
+pip install tqdm pandas h5py pyyaml joblib scikit-learn