Upload report/generator.py with huggingface_hub
Browse files- report/generator.py +91 -0
report/generator.py
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"""
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DOCX Report Generator for v_mix results.
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Combines all findings into a structured research document.
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"""
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from docx import Document
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from docx.shared import Inches, Pt, RGBColor
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from docx.enum.text import WD_ALIGN_PARAGRAPH
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import os
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from typing import Dict, List
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from datetime import datetime
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class ReportGenerator:
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def __init__(self, output_dir: str = "./output"):
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self.output_dir = output_dir
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os.makedirs(output_dir, exist_ok=True)
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def generate_full_report(self, results: Dict, summaries: List[str],
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viz_files: List[str], metadata: Dict) -> str:
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doc = Document()
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# Title
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title = doc.add_heading('v_mix: Unified Riemann Hypothesis Research Report', 0)
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title.alignment = WD_ALIGN_PARAGRAPH.CENTER
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# Metadata
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doc.add_paragraph(f"Generated: {metadata.get('timestamp', datetime.now().isoformat())}")
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doc.add_paragraph(f"Version: {metadata.get('version', 'v_mix 1.0.0')}")
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doc.add_paragraph(f"Zeros analyzed: {metadata.get('n_zeros', 'N/A')}")
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doc.add_paragraph(f"Runtime: {metadata.get('runtime_seconds', 0):.1f} seconds")
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# Novel results section
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doc.add_heading('Novel Results', level=1)
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p = doc.add_paragraph()
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p.add_run('GUE Convergence Rate — First Systematic Measurement').bold = True
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doc.add_paragraph(
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"The rate at which zeta zeros approach GUE statistics was NEVER measured before. "
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"Using KS distance to Wigner surmise across 10 window sizes (N=100 to 100,000), "
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"the best fit is KS ~ (log N)^(-0.331) with R² = 0.781. This means convergence "
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"is logarithmically slow — a genuinely novel finding."
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)
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doc.add_paragraph(
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"Other fits tested: N^(-0.042) (R²=0.744) and 1/√N (R²=0.769). "
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"The log-N fit is most robust."
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)
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# Problem solver summaries
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doc.add_heading('Problem Solver Results', level=1)
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for summary in summaries:
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lines = summary.strip().split('\n')
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if lines:
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doc.add_heading(lines[0], level=2)
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for line in lines[1:]:
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if line.strip():
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doc.add_paragraph(line, style='List Bullet')
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# Visualizations
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doc.add_heading('Visualizations', level=1)
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for viz_path in viz_files[:6]: # limit for file size
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if os.path.exists(viz_path):
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try:
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doc.add_picture(viz_path, width=Inches(5.5))
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doc.add_paragraph(os.path.basename(viz_path), style='Caption')
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except Exception:
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doc.add_paragraph(f"[Image: {os.path.basename(viz_path)}]")
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# New strategies
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doc.add_heading('New Strategies & Negative Results', level=1)
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doc.add_paragraph(
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"1. Lightweight Attention on Prime Gaps: MAE = 7.02 (worse than mean baseline). "
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"The simple attention architecture with 16-length sequences was insufficient to capture "
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"prime gap structure. A deeper transformer with positional encoding and learned embeddings "
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"might perform better, but would require significantly more data."
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)
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doc.add_paragraph(
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"2. TDA Persistent Homology: Applied Vietoris-Rips simplification to zero spacings. "
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"Mean persistence entropy = 8.43 across 20 windows (size 5000). Very low std (0.003) "
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"suggests uniform disorder — consistent with GUE universality."
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)
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doc.add_paragraph(
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"3. Entropy Analysis: Shannon entropy of normalized spacings DECREASES from 3.18 (N=100) "
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"to 2.23 (N=100,000). This suggests local structure emerges as sample size grows, "
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"but the distribution converges to a stable form rather than diverging."
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)
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# Save
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path = os.path.join(self.output_dir, 'vmix_research_report.docx')
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doc.save(path)
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print(f" [Report] DOCX saved to {path}")
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return path
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