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Robert Trenaman edited this page May 7, 2026 · 2 revisions

SCRIBE Developer Documentation

Developer Overview

This guide provides comprehensive information for developers working with the SCRIBE Resonance AI System, including setup, architecture, contribution guidelines, and advanced development topics.

Development Environment Setup

Prerequisites

  • Python 3.13+
  • Git
  • Virtual environment support

Quick Setup

# Clone the repository
git clone <repository-url>
cd scribe

# Setup development environment
./deploy.sh

# Activate virtual environment
source scribe_env/bin/activate

# Run tests
python3 validate_system.py

Development Dependencies

# Install development dependencies
pip install -r requirements.txt
pip install pytest black flake8 mypy

Project Structure

scribe/
├── src/                     # Source code
│   ├── core/                # System orchestration
│   ├── emitter/             # Audio signal generation
│   ├── listener/            # Audio capture
│   ├── processing/          # Signal analysis
│   ├── ai/                  # AI interpretation
│   ├── feedback/            # Learning system
│   ├── chat/                # User interface
│   ├── api/                 # REST API
│   ├── monitoring/          # Analytics
│   └── utils/               # Utilities
├── docs/                    # Documentation
├── tests/                   # Test files
├── scripts/                 # Utility scripts
├── main.py                  # Entry point
├── requirements.txt         # Dependencies
└── README.md               # Project info

Architecture Overview

Component Architecture

The SCRIBE system follows a modular, event-driven architecture:

System Controller (Core)
├── Emission Engine (Audio Output)
├── Listening Module (Audio Input)
├── Signal Processing (Feature Extraction)
├── AI Interpreter (Pattern Recognition)
├── Feedback Loop (Learning System)
└── Chat Interface (User Interaction)

Data Flow

Signal Generation → Audio Capture → Signal Processing → AI Interpretation → User Interface

Core Components

System Controller

from core.system_controller import ScribeSystemController
from utils.config import Config

# Initialize system
config = Config()
system = ScribeSystemController(config)

# Start system
await system.start()

# Perform scan
result = await system.perform_resonance_scan()

# Stop system
await system.stop()

Audio Components

from emitter.mock_audio import MockResonanceEmissionEngine, SignalConfig
from listener.mock_capture import MockMicroListeningModule

# Create components
emitter = MockResonanceEmissionEngine(config)
listener = MockMicroListeningModule(config)

# Initialize
await emitter.initialize()
await listener.initialize()

# Generate signal
signal_config = SignalConfig(signal_type='sine', frequency=440.0, duration=2.0)
emitted = await emitter.emit_signals([signal_config])

# Capture response
response = await listener.capture_response(duration=2.0)

Signal Processing

from processing.fft_analyzer import SignalProcessingLayer

# Create processor
processor = SignalProcessingLayer(config)
await processor.initialize()

# Analyze signal
results = await processor.analyze_signal(audio_data, emitted_signals)

# Access features
features = results['features']
fft_data = results['frequency_domain']

AI Interpretation

from ai.interpreter import ResonanceInterpretationEngine

# Create interpreter
interpreter = ResonanceInterpretationEngine(config)
await interpreter.initialize()

# Interpret results
interpretation = await interpreter.interpret_resonance(features, signal_history)

# Get insights
insights = interpretation['insights']
confidence = interpretation['confidence_scores']

🧪 Testing

Running Tests

# System validation
python3 validate_system.py

# Basic tests
python3 test_system.py

# Component tests
python3 -m pytest tests/

Test Structure

tests/
├── test_core/
├── test_emitter/
├── test_listener/
├── test_processing/
├── test_ai/
├── test_feedback/
└── test_chat/

Writing Tests

import pytest
from core.system_controller import ScribeSystemController
from utils.config import Config

@pytest.mark.asyncio
async def test_system_controller():
    config = Config()
    system = ScribeSystemController(config)
    
    await system.start()
    assert system.is_running
    
    result = await system.perform_resonance_scan()
    assert 'interpretation' in result
    
    await system.stop()
    assert not system.is_running

Adding New Components

1. Create Component Structure

mkdir src/new_component
touch src/new_component/__init__.py
touch src/new_component/main.py

2. Implement Component Interface

# src/new_component/main.py
import asyncio
import logging
from typing import Dict, Any, Optional

class NewComponent:
    def __init__(self, config):
        self.config = config
        self.logger = logging.getLogger(__name__)
        self.is_initialized = False
    
    async def initialize(self):
        """Initialize component"""
        self.logger.info("Initializing new component")
        self.is_initialized = True
    
    async def cleanup(self):
        """Cleanup resources"""
        self.is_initialized = False
        self.logger.info("New component cleaned up")
    
    async def get_status(self) -> Dict[str, Any]:
        """Get component status"""
        return {
            'initialized': self.is_initialized,
            'component': 'new_component'
        }

3. Update System Controller

# src/core/system_controller.py
from new_component.main import NewComponent

class ScribeSystemController:
    def __init__(self, config):
        # ... existing code ...
        self.new_component = NewComponent(config)
    
    async def start(self):
        # ... existing code ...
        await self.new_component.initialize()
    
    async def stop(self):
        # ... existing code ...
        await self.new_component.cleanup()

4. Add Tests

# tests/test_new_component/test_main.py
import pytest
from new_component.main import NewComponent
from utils.config import Config

@pytest.mark.asyncio
async def test_new_component():
    config = Config()
    component = NewComponent(config)
    
    await component.initialize()
    assert component.is_initialized
    
    status = await component.get_status()
    assert status['initialized']
    
    await component.cleanup()
    assert not component.is_initialized

Configuration

Configuration Classes

# src/utils/config.py
@dataclass
class NewComponentConfig:
    parameter1: float = 1.0
    parameter2: str = "default"
    enabled: bool = True

@dataclass
class Config:
    # ... existing configs ...
    new_component: NewComponentConfig = field(default_factory=NewComponentConfig)

Using Configuration

from utils.config import Config

config = Config()
param1 = config.new_component.parameter1
enabled = config.new_component.enabled

API Development

Adding New Endpoints

# src/api/main.py
from fastapi import APIRouter

router = APIRouter(prefix="/new-component", tags=["new-component"])

@router.post("/action")
async def new_component_action(
    request: NewComponentRequest,
    system: ScribeSystemController = Depends(get_system_controller)
):
    """Perform new component action"""
    try:
        result = await system.new_component.perform_action(request.dict())
        return {"status": "success", "result": result}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

# Add router to app
app.include_router(router)

API Models

# src/api/main.py
from pydantic import BaseModel, Field

class NewComponentRequest(BaseModel):
    parameter1: float = Field(..., description="Parameter 1")
    parameter2: str = Field(..., description="Parameter 2")

class NewComponentResponse(BaseModel):
    status: str
    result: Dict[str, Any]
    timestamp: str

Monitoring and Analytics

Adding Metrics

# src/monitoring/analytics.py
class AnalyticsEngine:
    def __init__(self, config):
        # ... existing metrics ...
        self.new_component_counter = Counter('new_component_actions_total', 'Total new component actions')
        self.new_component_duration = Histogram('new_component_action_duration_seconds', 'Action duration')
    
    async def record_new_component_action(self, action_type: str, duration: float):
        """Record new component action"""
        self.new_component_counter.labels(action_type=action_type).inc()
        self.new_component_duration.observe(duration)

Custom Analytics

# src/new_component/analytics.py
from monitoring.analytics import AnalyticsEngine

class NewComponentAnalytics:
    def __init__(self, analytics: AnalyticsEngine):
        self.analytics = analytics
        self.action_count = 0
        self.total_duration = 0.0
    
    async def record_action(self, action_type: str, duration: float):
        """Record action with custom analytics"""
        self.action_count += 1
        self.total_duration += duration
        
        await self.analytics.record_new_component_action(action_type, duration)
    
    def get_stats(self) -> Dict[str, Any]:
        """Get custom statistics"""
        return {
            'action_count': self.action_count,
            'avg_duration': self.total_duration / max(1, self.action_count),
            'total_duration': self.total_duration
        }

Debugging

Logging

import logging

logger = logging.getLogger(__name__)

class NewComponent:
    async def perform_action(self, params):
        logger.info(f"Starting action with params: {params}")
        
        try:
            result = await self._do_action(params)
            logger.info(f"Action completed successfully: {result}")
            return result
        except Exception as e:
            logger.error(f"Action failed: {e}", exc_info=True)
            raise

Debug Mode

# src/utils/config.py
@dataclass
class Config:
    debug_mode: bool = False

# Usage
if config.debug_mode:
    logging.basicConfig(level=logging.DEBUG)

Performance Profiling

import time
import functools

def profile_function(func):
    @functools.wraps(func)
    async def wrapper(*args, **kwargs):
        start_time = time.time()
        result = await func(*args, **kwargs)
        duration = time.time() - start_time
        logger.info(f"{func.__name__} took {duration:.3f}s")
        return result
    return wrapper

@profile_function
async def expensive_operation(self):
    # ... operation ...
    pass

Code Style and Standards

Python Style Guide

  • Follow PEP 8
  • Use type hints
  • Document all public methods
  • Use async/await for I/O operations

Example Code Style

from typing import Dict, List, Any, Optional
import logging
import asyncio

logger = logging.getLogger(__name__)

class ExampleComponent:
    """Example component for SCRIBE system.
    
    This component demonstrates proper coding standards.
    
    Attributes:
        config: Component configuration
        is_initialized: Whether component is initialized
    """
    
    def __init__(self, config: Dict[str, Any]) -> None:
        """Initialize the component.
        
        Args:
            config: Component configuration dictionary
        """
        self.config = config
        self.is_initialized = False
    
    async def initialize(self) -> None:
        """Initialize the component asynchronously."""
        try:
            logger.info("Initializing component")
            # ... initialization logic ...
            self.is_initialized = True
            logger.info("Component initialized successfully")
        except Exception as e:
            logger.error(f"Failed to initialize component: {e}")
            raise
    
    async def process_data(self, data: List[Any]) -> Dict[str, Any]:
        """Process input data and return results.
        
        Args:
            data: Input data to process
            
        Returns:
            Processing results
            
        Raises:
            ValueError: If data is invalid
        """
        if not data:
            raise ValueError("Data cannot be empty")
        
        # ... processing logic ...
        return {"processed": True, "count": len(data)}

Continuous Integration

GitHub Actions Example

# .github/workflows/ci.yml
name: CI

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.13
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: python3 validate_system.py
    - name: Run linting
      run: |
        pip install flake8 black
        flake8 src/
        black --check src/

📖 Documentation

Code Documentation

  • Use docstrings for all classes and methods
  • Include parameter and return type documentation
  • Provide usage examples

API Documentation

  • Update API docs when adding endpoints
  • Include request/response examples
  • Document error conditions

Wiki Updates

  • Update component documentation
  • Add new features to user guide
  • Update architecture diagrams

Contributing

Pull Request Process

  1. Fork the repository
  2. Create feature branch
  3. Make changes with tests
  4. Update documentation
  5. Submit pull request

Code Review Checklist

  • Code follows style guidelines
  • Tests are included and passing
  • Documentation is updated
  • No breaking changes
  • Performance impact considered

Issue Reporting

  • Use GitHub issues for bug reports
  • Include reproduction steps
  • Provide system information
  • Attach relevant logs

🔮 Advanced Topics

Custom Signal Processing

import numpy as np
from scipy import signal

class CustomProcessor:
    def custom_filter(self, audio_data: np.ndarray, cutoff: float) -> np.ndarray:
        """Apply custom Butterworth filter."""
        nyquist = 0.5 * self.sample_rate
        normal_cutoff = cutoff / nyquist
        b, a = signal.butter(4, normal_cutoff, btype='low', analog=False)
        filtered = signal.filtfilt(b, a, audio_data)
        return filtered

Machine Learning Integration

from sklearn.ensemble import RandomForestClassifier
import joblib

class MLModel:
    def __init__(self, model_path: str):
        self.model_path = model_path
        self.model = None
    
    async def load_model(self):
        """Load trained model."""
        self.model = joblib.load(self.model_path)
    
    async def predict(self, features: np.ndarray) -> Dict[str, float]:
        """Make predictions."""
        if self.model is None:
            await self.load_model()
        
        probabilities = self.model.predict_proba([features])[0]
        classes = self.model.classes_
        
        return dict(zip(classes, probabilities))

Real-time Processing

import asyncio
from collections import deque

class RealTimeProcessor:
    def __init__(self, buffer_size: int = 1024):
        self.buffer = deque(maxlen=buffer_size)
        self.processing = False
    
    async def add_data(self, data: np.ndarray):
        """Add data to buffer and trigger processing."""
        self.buffer.extend(data)
        
        if not self.processing and len(self.buffer) >= self.buffer_size // 2:
            self.processing = True
            asyncio.create_task(self._process_buffer())
    
    async def _process_buffer(self):
        """Process buffered data."""
        while len(self.buffer) >= self.buffer_size // 2:
            chunk = [self.buffer.popleft() for _ in range(self.buffer_size // 2)]
            await self._process_chunk(chunk)
        
        self.processing = False

External Resources

Last Updated: 2026-05-06
Developer Guide Version: 1.0.0
Status: Production Ready

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