Data Processing Thesis

The data_processing.py module represents the comprehensive data processing engine for ResonanceOS v6, providing utilities for processing text corpora, extracting HRV features, and preparing data for training and analysis. This system enables efficient batch processing of text files with automatic HRV vector extraction, quality analysis, and profile generation from existing content collections.

Technical Specifications

  • Processing Type: Batch Text Processing
  • HRV Integration: Automatic Vector Extraction
  • File Support: Multiple Format Processing
  • Export Options: JSON & CSV Output
  • Quality Analysis: Corpus Quality Assessment

Core Implementation Architecture

class DataProcessor: """Main data processing class for ResonanceOS""" def __init__(self, config_path: str = None): """Initialize the data processor""" self.hrv_extractor = HRVExtractor() self.config = self._load_config(config_path)
Text File Processing
Individual file processing with HRV extraction and basic statistics
Directory Batch Processing
Process entire directories of text files with configurable patterns
Corpus Profile Generation
Create HRV profiles from analyzed text collections
Quality Analysis
Comprehensive corpus quality assessment with scoring

Processing Pipeline

File Discovery
Locate and validate input files using configurable patterns
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Text Extraction
Read and parse text content with encoding handling
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HRV Analysis
Extract 8-dimensional HRV vectors using advanced analysis
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Statistics Calculation
Compute word counts, sentence analysis, and content metrics
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Result Aggregation
Combine results and prepare for export or analysis

HRV Feature Extraction

Text Processing Example

def process_text_file(self, file_path: str) -> Dict[str, Any]: try: with open(file_path, 'r', encoding='utf-8') as f: content = f.read() # Extract HRV features hrv_vector = self.hrv_extractor.extract(content) # Get basic statistics words = content.split() sentences = [s.strip() for s in content.split('.') if s.strip()] return { 'file_path': str(file_path), 'word_count': len(words), 'sentence_count': len(sentences), 'avg_sentence_length': sum(len(s.split()) for s in sentences) / len(sentences) if sentences else 0, 'hrv_vector': hrv_vector, 'content_preview': content[:200] + "..." if len(content) > 200 else content }

Extraction Results Structure

File Metadata

File path, encoding, and processing status

Basic Statistics

Word count, sentence count, average sentence length

HRV Vector

8-dimensional human-resonant value vector

Content Preview

First 200 characters for quick reference

Corpus Profile Generation

Profile Creation Process

def create_corpus_profile(self, results: List[Dict[str, Any]], profile_name: str) -> Dict[str, Any]: valid_results = [r for r in results if 'hrv_vector' in r and 'error' not in r] # Calculate average HRV vector hrv_vectors = [r['hrv_vector'] for r in valid_results] avg_hrv = [sum(dim[i] for dim in hrv_vectors) / len(hrv_vectors) for i in range(8)] # Calculate corpus statistics total_words = sum(r['word_count'] for r in valid_results) total_sentences = sum(r['sentence_count'] for r in valid_results) return { 'name': profile_name, 'description': f'Profile generated from {len(valid_results)} documents', 'hrv_vector': avg_hrv, 'metadata': { 'source_documents': len(valid_results), 'total_words': total_words, 'total_sentences': total_sentences, 'created_at': '2026-03-09T00:00:00Z', 'version': '1.0', 'corpus_type': 'generated' } }

Generated Profile Structure

{ "name": "Corporate_Brand_Voice", "description": "Profile generated from 127 documents", "hrv_vector": [0.73, 0.67, 0.45, 0.82, 0.56, 0.34, 0.61, 0.78], "metadata": { "source_documents": 127, "total_words": 45892, "total_sentences": 3847, "created_at": "2026-03-09T00:00:00Z", "version": "1.0", "corpus_type": "generated" } }

Corpus Quality Analysis

Quality Assessment Features

127
Document Count
45,892
Total Words
3,847
Total Sentences
0.78
Quality Score

Quality Score Calculation

def _calculate_quality_score(self, results: List[Dict[str, Any]]) -> float: # Factors for quality score avg_word_count = sum(r['word_count'] for r in results) / len(results) avg_sentence_count = sum(r['sentence_count'] for r in results) / len(results) # Score based on document length and variety length_score = min(avg_word_count / 500, 1.0) # Ideal around 500 words sentence_score = min(avg_sentence_count / 20, 1.0) # Ideal around 20 sentences # HRV variety score hrv_vectors = [r['hrv_vector'] for r in results] variety_score = self._calculate_hrv_variety(hrv_vectors) return (length_score + sentence_score + variety_score) / 3

Export Capabilities

Supported Export Formats

JSON Export
  • Full data structure preservation
  • HRV vectors as arrays
  • Metadata and statistics
  • Human-readable format
  • Easy API integration
    • CSV Export
  • Flattened HRV dimensions
  • Tabular data format
  • Spreadsheet compatibility
  • Statistical analysis ready
  • Data science workflows

    • Export Examples

    # JSON Export processor.export_results(results, 'output/analysis.json', 'json') # CSV Export with flattened HRV processor.export_results(results, 'output/analysis.csv', 'csv')

    Command Line Interface

    Available Commands

    python data_processing.py process --input ./corpus --output results.json
    Process directory and export results to JSON
    python data_processing.py process --input document.txt --format csv
    Process single file and export to CSV
    python data_processing.py analyze --input ./corpus --output quality_report.json
    Analyze corpus quality and generate report
    python data_processing.py profile --input ./corpus --profile-name "Brand Voice"
    Generate HRV profile from corpus
    python data_processing.py process --input ./corpus --pattern "*.md" --format json
    Process directory with custom file pattern

    Technical Implementation Thesis

    The data_processing.py module represents the comprehensive data processing engine for ResonanceOS v6, providing efficient batch processing capabilities with automatic HRV extraction, quality analysis, and profile generation. This implementation demonstrates sophisticated understanding of data processing workflows, file handling, and statistical analysis while maintaining clean, extensible architecture.

    Design Philosophy

    • Efficient Processing: Optimized for large-scale text corpus processing
    • Flexible Input: Support for files and directories with configurable patterns
    • Quality Focus: Built-in quality assessment and validation
    • Export Ready: Multiple output formats for different use cases

    Key Features

    Batch Processing

    Efficient processing of multiple files with parallel execution capabilities.

    HRV Integration

    Seamless integration with HRV extraction for comprehensive analysis.

    Quality Assessment

    Automated quality scoring and corpus analysis capabilities.

    Profile Generation

    Automatic creation of HRV profiles from analyzed text collections.