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Welcome to Dxns Hubs Harmonic Balancer Project

Our Mission

We are dedicated to providing access and opportunities for persons with disabilities in their work lives, fostering a sense of community and belonging.

Our Values

  • Compassion: We care deeply about the well-being of every individual.
  • Strength: Inspired by the resilience of our community.
  • Nurturing: Creating a supportive environment for growth and success.
  • Exploration: Encouraging innovation and new possibilities.

Get Involved

Join us in making a difference! Donate so we can get a new computer!

Follow Us

Stay updated with our latest news and events.

Harmoic Balancer Project

A mathematical tuning fork designed to help individuals find balance in various aspects of life. Our tool leverages key principles to optimize performance and efficiency:

  • R (Resonance): Identify and optimize patterns within systems.
  • F (Fuel Efficiency): Ensure efficient utilization of resources.
  • E (Energy Conversion): Optimize the conversion of inputs into outputs.
  • Golden Ratio: Utilize the golden ratio to achieve natural balance and harmony.

Equation: $$\Phi = \sqrt{(R \cdot F^2) + E^2}$$

This equation can be adapted to balance other equations by incorporating different constants, such as:

  • Equation: $$\sqrt{(R \cdot F^2) + E^2} \cdot \Psi$$
  • Equation: $$\sqrt{(R \cdot F^2) + E^2} \cdot \pi$$
  • Equation: $$\sqrt{(R \cdot F^2) + E^2} \cdot \phi$$
  • Equation: $$\sqrt{(R \cdot F^2) + E^2} \cdot e$$

We also incorporate fundamental mathematical constants like Pi (π), Euler’s number €, Phi (φ), and Psi (ψ) to explore their potential in achieving optimal balance and efficiency. Join us in exploring the science of balance and harmony to enhance productivity and well-being.

  • Features
    • Multi-agent simulation of interconnected groups
    • Various network topologies (small-world, scale-free, random)
      • External shock simulations (pulse, sine, step, complex)
      • System complexity and resilience analysis
      • Visualization of system dynamics and resilience metrics
  • Examples: Added multiple examples demonstrating how to use the HarmonicBalancer class in different fields:
    • Mathematical Constants: Example using π.
    • Scientific Applications: Example using an exponential function.
    • Musical Applications: Example using a sine function.
    • Image Processing: Example simulating an image processing function.
  • Testing: Instructions on how to run the tests.
  • Contributing: Information on how to contribute to the project.
  • License: License information.

Explanation

  • app.py: The Flask application that serves the web interface and runs the tests.
  • harmonic_balancer.py: Contains the HarmonicBalancer class.
  • ecosystem.py: Contains the EnhancedHumanQuantumEcosystem class.
  • quantum_reactor_simulation.py: Contains the QuantumReactor class and its simulation methods.
  • quantum_system.py: Contains the QuantumSystem class.
  • analysis.py: Contains functions for analyzing the results.
  • field_applications.py: Provides example applications of the HarmonicBalancer.
  • visualization.py: Contains functions for visualizing the results.
  • requirements.txt: Lists the project dependencies.
  • README.md: Provides an overview of the project, installation instructions, usage examples, and contribution guidelines.
  • static/index.html: The HTML5 file that serves as the frontend for the web application.
  • tests: Directory containing test scripts.
    • test_harmonic_balancer.py: Tests for the HarmonicBalancer class.
    • test_ecosystem.py: Tests for the EnhancedHumanQuantumEcosystem class.
    • test_quantum_reactor.py: Tests for the QuantumReactor class.
  • CONTRIBUTING.md: Provides guidelines for contributing to the project.
  • docs: Directory for documentation files.
    • The_Foundation_of_Resonant_Harmonics.pdf: PDF file containing information on the findings and base equation.
    • average_complexity_over_time.png: Image file.
    • complexity_over_time.png: Image file.

Usage Examples

Mathematical Constants

import numpy as np
from harmonic_balancer import HarmonicBalancer

def pi_objective_function(vector, param):
    return np.sum(vector) * np.pi

balancer = HarmonicBalancer(num_qubits=4, max_iterations=100, harmony_memory_size=10, objective_function=pi_objective_function)
best_solution, best_score = balancer.run_experiment()

print("Best solution:", best_solution)
print("Best score:", best_score)

Scientific Applications

import numpy as np
from harmonic_balancer import HarmonicBalancer

def exp_objective_function(vector, param):
    return np.sum(np.exp(vector))

balancer = HarmonicBalancer(num_qubits=4, max_iterations=100, harmony_memory_size=10, objective_function=exp_objective_function)
best_solution, best_score = balancer.run_experiment()

print("Best solution:", best_solution)
print("Best score:", best_score)

Musical Applications

import numpy as np
from harmonic_balancer import HarmonicBalancer

def sine_objective_function(vector, param):
    return np.sum(np.sin(vector))

balancer = HarmonicBalancer(num_qubits=4, max_iterations=100, harmony_memory_size=10, objective_function=sine_objective_function)
best_solution, best_score = balancer.run_experiment()

print("Best solution:", best_solution)
print("Best score:", best_score)

Image Processing

import numpy as np
from harmonic_balancer import HarmonicBalancer

def image_processing_objective_function(vector, param):
    # Simulate an image processing function
    return np.sum(vector) * 255  # Example: scaling pixel values

balancer = HarmonicBalancer(num_qubits=4, max_iterations=100, harmony_memory_size=10, objective_function=image_processing_objective_function)
best_solution, best_score = balancer.run_experiment()

print("Best solution:", best_solution)
print("Best score:", best_score)

Web Application

Setting Up The Web Application To set up the web application, follow these steps:

1. Install Dependencies: Ensure all dependencies are installed

To install the required dependencies, run:

pip install -r requirements.txt
2. Run the Flask Application

python app.py

3. Access the Frontend: Open your browser and go to http://127.0.0.1:5000/ to access the frontend

Running Tests via the Web Interface

  1. Open the Web Interface: Go to http://127:.0.0.1:5000/ in your web browser.
  2. Run Tests: Click the "Run All Tests" button to start the tests
  3. View Results: The test results will be displayed in the "Test Results" section on the page.

Testing

Run the tests using:

python -m unitest descover tests

Contributing

If you would like to contribute to this project, please fork the repository and sumbit a pull request.

License

This project is Licensed under the MIT License