The following Python scripts serve as prototypes and demonstrations for the concepts outlined in this blueprint. They illustrate the core logic for the back-end orchestration and a tangible visualization for the front-end user experience.
main.py: System Orchestrator
This script represents the entry point for the creative system. It orchestrates the initialization and interaction of all major components, including audio processing, AI integrations, particle management, rendering, and logging. It showcases the multi-threaded approach required to handle simultaneous inputs and processes.
# main.py - System Orchestrator
import threading, queue, time, os
import numpy as np
# Dependencies: pygame, numpy, sounddevice, SpeechRecognition, pyttsx3, openai, etc.
# --- CONFIGURATION ---
SAMPLE_RATE = 44100
MAX_PARTICLES = 2000
GROK_API_KEY = os.getenv("GROK_API_KEY", "your_api_key_here")
# --- GLOBAL VARIABLES ---
log_queue = queue.Queue()
audio_queue = queue.Queue()
particle_lock = threading.Lock()
# --- AUDIO CALLBACK ---
def audio_callback(indata, frames, time_info, status):
# This function would process real-time audio with FFT
# to find the dominant frequency and put it in audio_queue.
# For brevity, the implementation is omitted here.
pass
# --- COMMAND PROCESSING ---
def process_command(command):
# Processes text commands to spawn particles or trigger events.
log_queue.put(f"Processing command: {command}")
# Implementation omitted.
# --- AI LEARNING ---
def learn_from_text(user_text):
# Interfaces with offline and online AI models to generate responses
# and adapt system parameters.
log_queue.put(f"Learning from: {user_text}")
# Implementation omitted.
# --- MAIN THREADS ---
def speech_recognition_loop():
while True:
# text = recognize_speech() # from microphone
# if text:
# process_command(text) or learn_from_text(text)
time.sleep(1)
def audio_processing_loop():
# with sd.InputStream(callback=audio_callback):
while True:
# freq = audio_queue.get()
# adjust_system_based_on_audio(freq)
time.sleep(0.1)
def main():
# This main function initializes and starts all threads:
# - Renderer (like cosmo.py)
# - Speech Recognition Thread
# - Audio Processing Thread
# - Log GUI (Tkinter)
print("Starting the Refined Creative Math-Driven System...")
# Full implementation details in the provided source file.
if __name__ == "__main__":
main()
cosmo.py: UI Visualization Prototype
This script creates the "Cosmo Client," a UI window using Pygame that demonstrates the front-end vision. It includes a live feed of particles that react to simulated environmental data, dancing and clustering based on the system's conversion logic. It also features a UI with simulated tabs for viewing live token creation and the conversion process log. The "COSMO" header pulses with light on each conversion event, providing a tangible link between the environment and the interface.
Note: This is a self-contained prototype. It simulates environmental audio and a blockchain for demonstration purposes. It uses Torch for GPU-accelerated particle physics to handle complex, gravity-like clustering forces inspired by the MDUT framework.
# cosmo.py - UI Visualization Prototype
# Full, runnable script. Requires: pip install pygame torch numpy scipy
import pygame, sys, numpy as np, uuid, torch
from scipy.integrate import odeint
# --- CONSTANTS ---
WIDTH, HEIGHT = 800, 600
FPS = 60
PARTICLE_COUNT_MAX = 500
PHI = (1 + np.sqrt(5)) / 2
G_ANALOG = 0.0005 # Tuned for subtle clustering
# --- UTILITY FUNCTION ---
def wavelength_to_rgb(wavelength, gamma=0.8):
# Converts a wavelength in nm to an RGB tuple (clamped 0-255)
wavelength = np.clip(wavelength, 380, 780)
if 380 <= wavelength <= 440:
attenuation, r, g, b = 0.3 + 0.7 * (wavelength - 380) / (440 - 380), -(wavelength - 440) / (440 - 380), 0.0, 1.0
elif 440 < wavelength <= 490:
attenuation, r, g, b = 1.0, 0.0, (wavelength - 440) / (490 - 440), 1.0
elif 490 < wavelength <= 510:
attenuation, r, g, b = 1.0, 0.0, 1.0, -(wavelength - 510) / (510 - 490)
elif 510 < wavelength <= 580:
attenuation, r, g, b = 1.0, (wavelength - 510) / (580 - 510), 1.0, 0.0
elif 580 < wavelength <= 645:
attenuation, r, g, b = 1.0, 1.0, -(wavelength - 645) / (645 - 580), 0.0
else: # 645 < wavelength <= 780
attenuation, r, g, b = 0.3 + 0.7 * (780 - wavelength) / (780 - 645), 1.0, 0.0, 0.0
r, g, b = (r * attenuation)**gamma, (g * attenuation)**gamma, (b * attenuation)**gamma
return (int(r * 255), int(g * 255), int(b * 255))
class CosmoClient:
def __init__(self):
pygame.init()
self.screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption('Cosmo Client - VLCL (Full GPU-Accelerated)')
# ... (rest of __init__ method)
def simulate_env_sound(self):
# ... (simulates audio frequency)
return np.abs(440 + 100 * np.sin(self.t) + 50 * np.random.randn())
def convert_to_light_token(self, freq):
# ... (maps frequency to color, creates token, spawns particle)
pass
def update_particles(self):
# ... (updates particle physics using Torch on GPU, including chaos and clustering)
pass
def draw_particles(self):
# ... (draws particles to the screen)
pass
def run(self):
# ... (main game loop: handles events, drawing, and updates)
pass
if __name__ == '__main__':
# Full implementation details are in the provided source file.
# This is a conceptual representation.
print("Conceptual CosmoClient running...")
# client = CosmoClient()
# client.run()