hrv_extraction.py - ResonanceOS v6 Documentation

HRV Extraction Thesis

The hrv_extraction.py module provides comprehensive examples demonstrating Human-Resonant Value (HRV) vector extraction from text content using ResonanceOS v6. This educational script showcases the 8-dimensional HRV analysis system, including basic extraction, detailed analysis, batch processing, comparative analysis, and quality assessment - all designed to help users understand how to extract and interpret human resonance metrics from any text content.

Technical Specifications

Purpose: HRV Vector Extraction Education
Dimensions: 8-Dimensional Analysis
Features: Basic, Detailed, Batch, Comparative Analysis
Quality: Content Quality Assessment
Output: Detailed HRV Metrics & Recommendations

Core Implementation

from resonance_os.profiles.hrv_extractor import HRVExtractor

def basic_extraction_example():
    """Demonstrate basic HRV extraction from simple text"""
    print("🔍 Basic HRV Extraction Example")
    
    # 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...",
        "In the rapidly evolving landscape of digital transformation..."
    ]
    
    for text in sample_texts:
        # 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}")
                

Basic Extraction

Simple HRV vector extraction from individual text samples with interpretation

Detailed Analysis

Comprehensive dimension-by-dimension analysis with quality assessment

Batch Processing

Efficient processing of multiple texts with comparative results

Quality Assessment

Content quality evaluation using HRV metrics with recommendations

HRV Dimensions Analysis

8-Dimensional HRV Vector

def interpret_hrv_vector(hrv_vector):
    """Interpret HRV vector characteristics"""
    dimensions = [
        "Sentence Variance", "Emotional Valence", "Emotional Intensity",
        "Assertiveness", "Curiosity", "Metaphor Density", 
        "Storytelling", "Active Voice"
    ]
    
    interpretations = []
    for i, (dimension, value) in enumerate(zip(dimensions, hrv_vector)):
        if value > 0.7:
            interpretations.append(f"High {dimension.lower().replace(' ', '_')}")
        elif value < 0.3:
            interpretations.append(f"Low {dimension.lower().replace(' ', '_')}")
    
    return ", ".join(interpretations) if interpretations else "Balanced"
                    

Sentence Variance

0.73

Emotional Valence

0.67

Emotional Intensity

0.45

Assertiveness

0.82

Curiosity

0.56

Metaphor Density

0.34

Storytelling

0.61

Active Voice

0.78

Analysis Workflow

Text Input

Provide text content for HRV analysis

↓

HRV Extraction

Extract 8-dimensional HRV vector

↓

Dimension Analysis

Analyze each dimension individually

↓

Quality Assessment

Evaluate overall content quality

↓

Recommendations

Generate improvement suggestions

Detailed Analysis Example

Comprehensive HRV Analysis

def detailed_analysis_example():
    """Demonstrate detailed HRV analysis with multiple metrics"""
    
    # Complex text for detailed analysis
    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.
    """
    
    # Extract HRV vector
    hrv_vector = extractor.extract(analysis_text)
    
    # Detailed analysis
    dimensions = [
        "Sentence Variance", "Emotional Valence", "Emotional Intensity",
        "Assertiveness", "Curiosity", "Metaphor Density", 
        "Storytelling", "Active Voice"
    ]
    
    for i, (dimension, value) in enumerate(zip(dimensions, hrv_vector)):
        # Determine level
        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})")
                    

Quality Assessment System

Content Quality Classification

def quality_assessment_example():
    """Demonstrate content quality assessment using HRV"""
    
    # Quality classification
    avg_score = sum(hrv_vector) / len(hrv_vector)
    
    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"
                    

Quality Grade System

Score > 0.7

Score > 0.6

Score > 0.5

Score ≤ 0.5

Batch Processing Example

Multi-Text Analysis

def batch_extraction_example():
    """Demonstrate batch HRV extraction from multiple texts"""
    
    # Batch of texts to analyze
    batch_texts = {
        "business_report": "Q3 2024 Financial Performance Report...",
        "creative_story": "In a world where dreams and reality intertwine...",
        "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
                    

Batch Analysis Results

Texts Processed

0.687

Average Score

100%

Success Rate

Style Types

Comparative Analysis

Style Comparison Features

def comparative_analysis_example():
    """Demonstrate comparative analysis between texts"""
    
    # Texts for comparison
    comparison_texts = [
        ("Formal Business", "The quarterly financial report indicates significant growth..."),
        ("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...")
    ]
                    

Style Comparison Table

Style           Score    Sent Var Emo Val Emo Int Assert  Curious Metaphor Story   Active
-----------------------------------------------------------------------------------------------
Formal Business 0.634   0.68    0.71    0.52    0.79    0.63    0.41    0.58    0.74
Casual Blog     0.756   0.79    0.73    0.56    0.71    0.69    0.48    0.74    0.82
Technical Manual 0.598  0.62    0.65    0.44    0.81    0.55    0.33    0.59    0.76
Creative Writing 0.723  0.76    0.69    0.51    0.73    0.67    0.71    0.78    0.68

AI-Powered Recommendations

Content Optimization 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[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")
                    

Vary sentence lengths for better readability and engagement
Add more positive elements to engage readers emotionally
Use more confident and direct language for stronger impact
Add questions or curiosity-inducing elements to maintain interest
Consider adding metaphors for better engagement and retention
Incorporate storytelling elements for better reader connection
Use more active voice for clearer and more direct communication

Technical Implementation Thesis

The hrv_extraction.py module serves as a comprehensive educational gateway to understanding Human-Resonant Value (HRV) vector extraction in ResonanceOS v6. This implementation demonstrates the complete workflow of extracting 8-dimensional HRV vectors from text content, interpreting the results, and generating actionable recommendations for content optimization. The module showcases advanced understanding of linguistic analysis, quality assessment, and comparative text analysis while providing clear, educational examples that help users master the HRV analysis system.

Educational Design Philosophy

Progressive Learning: From basic to advanced analysis techniques
Practical Examples: Real-world text analysis scenarios
Comprehensive Coverage: All HRV dimensions and interpretation methods
Actionable Insights: Practical recommendations for content improvement

Key Learning Objectives

HRV Extraction

Master the process of extracting 8-dimensional HRV vectors from text.

Dimension Analysis

Understand each HRV dimension and its impact on human resonance.

Quality Assessment

Learn to evaluate content quality using HRV metrics.

Comparative Analysis

Compare different writing styles using HRV vector analysis.