hrv_extraction.py - ResonanceOS v6 Documentation

HRV Extraction Thesis

The hrv_extraction.py module demonstrates the fundamental Human-Resonant Value (HRV) vector extraction capabilities of ResonanceOS v6, including basic extraction, detailed analysis, batch processing, comparative analysis, and quality assessment. This comprehensive example showcases how the system analyzes text across 8 dimensions of human response, providing quantitative insights into content effectiveness, style characteristics, and quality metrics - all designed to help users understand and optimize their content for maximum human engagement and resonance.

Technical Specifications

HRV Dimensions: 8-dimensional human response vectors
Analysis Types: Basic extraction, detailed analysis, batch processing
Quality Assessment: Automated content quality scoring
Comparative Analysis: Style comparison and differentiation
Recommendations: AI-driven content improvement suggestions

Core HRV Extraction

from resonance_os.profiles.hrv_extractor import HRVExtractor

def basic_extraction_example():
    """Demonstrate basic HRV extraction from simple text"""
    
    # Initialize HRV extractor
    extractor = HRVExtractor()
    
    # Sample texts for analysis
    sample_texts = [
        "This is a simple test sentence.",
        "The revolutionary impact of artificial intelligence transforms modern business operations through data-driven insights.",
        "Once upon a time, in a magical forest where sunlight danced through emerald leaves like golden fairies..."
    ]
    
    for i, text in enumerate(sample_texts, 1):
        # Extract HRV vector
        hrv_vector = extractor.extract(text)
        
        # Display results
        print(f"HRV Vector: {[round(x, 3) for x in hrv_vector]}")
        print(f"Average Score: {sum(hrv_vector) / len(hrv_vector):.3f}")
        
        # Interpret results
        interpretation = interpret_hrv_vector(hrv_vector)
        print(f"Interpretation: {interpretation}")
                

8-Dimensional Analysis

Comprehensive analysis across sentence variance, emotional valence, intensity, and more

Real-Time Processing

Instant HRV vector extraction from any text content

Quality Assessment

Automated content quality scoring and classification

Style Analysis

Detailed writing style characteristics and patterns

HRV Extraction Workflow

1. Text Input

Provide text content for HRV analysis

↓

2. Feature Extraction

Analyze linguistic and semantic features

↓

3. Vector Generation

Create 8-dimensional HRV vector

↓

4. Analysis & Insights

Provide interpretation and recommendations

HRV Dimensions Explained

8 Dimensions of Human Response

def interpret_hrv_vector(hrv_vector):
    """Interpret HRV vector characteristics"""
    dimensions = [
        "Sentence Variance", # Variety in sentence structure and length
        "Emotional Valence", # Positive vs negative emotional tone
        "Emotional Intensity", # Strength of emotional expression
        "Assertiveness", # Confidence and directness
        "Curiosity", # Questioning and exploratory elements
        "Metaphor Density", # Use of figurative language
        "Storytelling", # Narrative elements and engagement
        "Active Voice" # Direct vs passive construction
    ]
    
    for i, (dimension, value) in enumerate(zip(dimensions, hrv_vector)):
        if value > 0.7:
            level = "High"
            indicator = "🟢"
        elif value > 0.4:
            level = "Medium"
            indicator = "🟡"
        else:
            level = "Low"
            indicator = "🔴"
        
        print(f"{indicator} {dimension:<20}: {value:.3f} ({level})")
                    

Dimension Breakdown

Sentence Variance

Variety in sentence structure and length

Emotional Valence

Positive vs negative emotional tone

Emotional Intensity

Strength of emotional expression

Assertiveness

Confidence and directness

Curiosity

Questioning and exploratory elements

Metaphor Density

Use of figurative language

Storytelling

Narrative elements and engagement

Active Voice

Direct vs passive construction

Content Quality Assessment

Automated Quality Scoring

def quality_assessment_example():
    """Demonstrate content quality assessment using HRV"""
    
    quality_texts = [
        ("Poor Quality", "bad text not good"),
        ("Fair Quality", "This is a simple text with basic information."),
        ("Good Quality", "This document provides important information about the project."),
        ("Excellent Quality", "The comprehensive analysis reveals significant insights into market dynamics...")
    ]
    
    for quality_name, text in quality_texts:
        hrv_vector = extractor.extract(text)
        avg_score = sum(hrv_vector) / len(hrv_vector)
        
        # Quality classification
        if avg_score > 0.7:
            grade = "A"
            assessment = "Excellent"
        elif avg_score > 0.6:
            grade = "B"
            assessment = "Good"
        elif avg_score > 0.5:
            grade = "C"
            assessment = "Fair"
        else:
            grade = "D"
            assessment = "Poor"
        
        print(f"{quality_name:<15} {grade:<5} {avg_score:<8.3f} {assessment}")
                    

Quality Classification

Excellent (A)

HRV score > 0.7 - High engagement

Good (B)

HRV score 0.6-0.7 - Solid content

Fair (C)

HRV score 0.5-0.6 - Acceptable quality

Poor (D)

HRV score < 0.5 - Needs improvement

Comparative Analysis

Style Comparison & Differentiation

def comparative_analysis_example():
    """Demonstrate comparative analysis between texts"""
    
    comparison_texts = [
        ("Formal Business", "The quarterly financial report indicates significant growth in revenue streams..."),
        ("Casual Blog", "Hey everyone! I'm super excited to share some amazing news..."),
        ("Technical Manual", "Initialize the system by executing the configuration script..."),
        ("Creative Writing", "As dawn broke over the misty mountains, Sarah discovered a hidden path...")
    ]
    
    # Create comparison table
    dimensions = ["Sent Var", "Emo Val", "Emo Int", "Assert", 
                  "Curious", "Metaphor", "Story", "Active"]
    
    print(f"{'Style':<15} {'Score':<8} " + " ".join(f"{dim:<8}" for dim in dimensions))
    
    for style_name, text in comparison_texts:
        hrv_vector = extractor.extract(text)
        avg_score = sum(hrv_vector) / len(hrv_vector)
        values = [f"{x:<8.3f}" for x in hrv_vector]
        print(f"{style_name:<15} {avg_score:<8.3f} " + " ".join(values))
                    

Analysis Capabilities

Style Comparison

Compare different writing styles side-by-side

Dimension Analysis

Identify key differentiating factors

Pattern Recognition

Discover style patterns and characteristics

Quality Metrics

Assess relative quality across texts

Batch HRV Processing

Efficient Bulk Analysis

def batch_extraction_example():
    """Demonstrate batch HRV extraction from multiple texts"""
    
    batch_texts = {
        "business_report": "Q3 2024 Financial Performance Report. Revenue increased by 23%...",
        "creative_story": "In a world where dreams and reality intertwine, a young artist discovers...",
        "technical_documentation": "The system architecture consists of three primary components...",
        "marketing_content": "Transform your business with our revolutionary AI-powered solutions!"
    }
    
    results = {}
    for text_type, text in batch_texts.items():
        hrv_vector = extractor.extract(text.strip())
        results[text_type] = hrv_vector
        print(f"{text_type}: HRV {[round(x, 3) for x in hrv_vector]}")
    
    # Compare results
    print(f"{'Content Type':<20} {'Avg Score':<10} {'Style':<20}")
    for text_type, hrv_vector in results.items():
        avg_score = sum(hrv_vector) / len(hrv_vector)
        style = determine_style(hrv_vector)
        print(f"{text_type:<20} {avg_score:<10.3f} {style:<20}")
                    

Batch Processing Features

Bulk Analysis

Process multiple texts simultaneously

Style Classification

Automatic style categorization

Comparison Tables

Structured result comparison

Performance Metrics

Processing efficiency tracking

AI-Driven Recommendations

Content Improvement Suggestions

def generate_recommendations(hrv_vector):
    """Generate recommendations based on HRV analysis"""
    recommendations = []
    
    # Analyze each dimension
    if hrv_vector[0] < 0.4:  # Sentence Variance
        recommendations.append("Vary sentence lengths for better readability")
    
    if hrv_vector[1] < 0.3:  # Emotional Valence
        recommendations.append("Add more positive elements to engage readers")
    
    if hrv_vector[2] < 0.4:  # Emotional Intensity
        recommendations.append("Increase emotional impact through stronger language")
    
    if hrv_vector[3] < 0.5:  # Assertiveness
        recommendations.append("Use more confident and direct language")
    
    if hrv_vector[4] < 0.4:  # Curiosity
        recommendations.append("Add questions or curiosity-inducing elements")
    
    if hrv_vector[5] < 0.3:  # Metaphor Density
        recommendations.append("Consider adding metaphors for better engagement")
    
    if hrv_vector[6] < 0.4:  # Storytelling
        recommendations.append("Incorporate storytelling elements for better connection")
    
    if hrv_vector[7] < 0.6:  # Active Voice
        recommendations.append("Use more active voice for clearer communication")
    
    return "; ".join(recommendations) if recommendations else "Content is well-balanced"
                    

Recommendation Categories

Structure Improvements

Sentence variance and organization

Emotional Enhancement

Valence and intensity optimization

Clarity & Directness

Assertiveness and active voice

Engagement Boosters

Curiosity and storytelling elements

Figurative Language

Metaphor and creative expression

Balance Assessment

Overall content equilibrium

Detailed HRV Analysis

Comprehensive Text Analysis

def detailed_analysis_example():
    """Demonstrate detailed HRV analysis with multiple metrics"""
    
    analysis_text = """
    The integration of artificial intelligence into enterprise operations represents a paradigm 
    shift in how organizations approach strategic decision-making and operational efficiency. 
    By leveraging machine learning algorithms and advanced analytics, companies can now process 
    vast amounts of data to uncover insights that were previously hidden in complex patterns.
    """
    
    hrv_vector = extractor.extract(analysis_text)
    
    # Detailed analysis with indicators
    dimensions = ["Sentence Variance", "Emotional Valence", "Emotional Intensity",
                  "Assertiveness", "Curiosity", "Metaphor Density", 
                  "Storytelling", "Active Voice"]
    
    for i, (dimension, value) in enumerate(zip(dimensions, hrv_vector)):
        if value > 0.7:
            level = "High"
            indicator = "🟢"
        elif value > 0.4:
            level = "Medium"
            indicator = "🟡"
        else:
            level = "Low"
            indicator = "🔴"
        
        print(f"{indicator} {dimension:<20}: {value:.3f} ({level})")
    
    # Overall assessment
    avg_score = sum(hrv_vector) / len(hrv_vector)
    print(f"\nOverall HRV Score: {avg_score:.3f}")
    
    # Quality assessment
    if avg_score > 0.7:
        quality = "Excellent"
    elif avg_score > 0.6:
        quality = "Good"
    elif avg_score > 0.5:
        quality = "Acceptable"
    else:
        quality = "Needs Improvement"
    
    print(f"Content Quality: {quality}")
                    

Analysis Features

Visual Indicators

Color-coded dimension levels

Quality Classification

Automated quality assessment

Dimension Breakdown

Individual metric analysis

Improvement Suggestions

AI-driven recommendations

Technical Implementation Thesis

The hrv_extraction.py module represents the fundamental HRV vector extraction capabilities of ResonanceOS v6, demonstrating how the system analyzes text across 8 dimensions of human response to provide quantitative insights into content effectiveness, style characteristics, and quality metrics. This implementation showcases sophisticated understanding of linguistic analysis, human psychology, and content optimization while providing practical tools for writers, marketers, and content creators to understand and improve their text for maximum human engagement and resonance.

HRV Extraction Philosophy

Multi-Dimensional Analysis: 8 dimensions capture comprehensive human response
Quantitative Metrics: Numerical scores for objective assessment
Actionable Insights: Specific recommendations for improvement
Style Awareness: Understanding of different writing styles

Key Extraction Features

8-Dimensional Analysis

Comprehensive human response vector extraction.

Quality Assessment

Automated content quality scoring and classification.

Comparative Analysis

Style comparison and differentiation capabilities.

Recommendation Engine

AI-driven content improvement suggestions.