A Python library for calculating Information Flux at the Cauchy Horizon
Project description
The Ali Integral: Observable Future Information (OFI)
🌌 Abstract
This project provides a computational implementation of Vision Theory, a framework unifying General Relativity and Quantum Information Theory. We introduce a new physical metric, $I_{Ali}$ (The Ali Integral), which quantifies the maximum amount of information ("History of the Future") an observer can decode while falling towards the Cauchy Horizon of a Black Hole.
The simulation resolves the infinite energy paradox by applying:
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Shannon-Hartley Theorem with dynamic gravitational SNR.
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Lloyd Limit for computational bounds.
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Thermal Destruction Criteria (Backreaction/Crash).
🚀 Features
- Rigorous Physics: Models gravitational blueshift ($g \propto 1/r$), quadratic energy flux ($F \propto g^2$), and dynamic bandwidth.
- Comparison Engine: Simulates infall for Stellar, Supermassive (Sgr A*), and Ultramassive (TON 618) Black Holes.
- Scientific Output: Automatically generates graphs and a fully formatted PDF research paper (MIT-style).
📂 Project Structure
src/physics.py: Core logic for Shannon Capacity and Integration.src/plotting.py: Matplotlib plotting engine for logarithmic scaling and LaTeX rendering.src/config.py: Centralized configuration for physical constants ($B_0$, $C_{limit}$, $F_{crit}$).main.py: Orchestrator script.
🛠️ Installation & Usage
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Clone the repository:
git clone https://github.com/YourUsername/Ali-Integral-Project.git cd Ali-Integral-Project
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Install dependencies:
pip install -r requirements.txt
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Run the simulation:
python main.py -
View Results: Check the
output/folder for the generated PDF paper and high-resolution graphs.
📊 The Ali Integral Formula
The total Observable Future Information (OFI) is defined as:
$$ I_{Ali} = \int_{0}^{\tau_{crash}} \min \left( B(\tau) \log_2(1 + SNR(\tau)), \ C_{Lloyd} \right) d\tau $$
Where $C_{Lloyd}$ is the fundamental quantum limit of computation.
Author: Ali | Version: 10.0 (Final Scientific Release)
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