#!/usr/bin/env python3 """ s音文明基因验证工具包 Silk Road S-Phoneme Civilization Gene Verification Toolkit 用于验证和分析丝绸之路s音文化哈希理论的工具集合 """ import re import json from collections import defaultdict, Counter from typing import Dict, List, Tuple, Optional import Levenshtein from dataclasses import dataclass from pathlib import Path @dataclass class SPhonemeWord: """s音词汇数据结构""" word: str language: str s_variant: str meaning: str era: str region: str cultural_context: str confidence_score: float = 1.0 class SPhonemeAnalyzer: """s音分析器核心类""" def __init__(self): # s音及其变体定义 self.s_variants = { 's': 'standard', 'ś': 'palatalized', 'š': 'retroflex', 'sh': 'english_sh', 'x': 'chinese_x', 'z': 'voiced', 'c': 'latin_c', 'ss': 'geminate', 'sc': 'latin_sc' } # 丝绸之路核心s音词汇库 self.silk_road_lexicon = self._initialize_lexicon() def _initialize_lexicon(self) -> List[SPhonemeWord]: """初始化丝绸之路s音词汇库""" lexicon_data = [ # 中原起点 ("丝", "chinese", "s", "silk", "ancient", "central_china", "core_commodity"), ("商", "chinese", "sh", "merchant", "ancient", "central_china", "trade_identity"), ("缫", "chinese", "s", "reel_silk", "ancient", "central_china", "silk_technology"), ("莎车", "chinese", "s", "Shache_kingdom", "ancient", "xinjiang", "silk_road_city"), # 西域中转 ("粟特", "chinese", "s", "Sogdian", "medieval", "central_asia", "merchant_ethnicity"), ("sart", "sogdian", "s", "merchant", "medieval", "central_asia", "trade_profession"), ("sūtra", "sanskrit", "s", "scripture", "ancient", "india", "buddhist_text"), # 中亚枢纽 ("Samarkand", "persian", "s", "city_name", "ancient", "uzbekistan", "trade_hub"), ("sesame", "english", "s", "sesame", "ancient", "mesopotamia", "trade_crop"), ("śaśama", "sanskrit", "ś", "sesame", "ancient", "india", "sanskrit_crop"), # 西亚节点 ("沙门", "chinese", "sh", "buddhist_monk", "ancient", "china", "buddhist_title"), ("śramaṇa", "sanskrit", "ś", "ascetic", "ancient", "india", "religious_practitioner"), ("Syria", "english", "s", "Syria", "ancient", "levant", "geographical_region"), ("Sūriyā", "arabic", "s", "Syria", "medieval", "levant", "arabic_geography"), # 欧洲终端 ("silk", "english", "s", "silk", "medieval", "europe", "luxury_good"), ("satin", "english", "s", "satin", "medieval", "europe", "fine_fabric"), ("saltpeter", "english", "s", "saltpeter", "medieval", "europe", "chemical_compound"), # 关键族群 ("Śākya", "sanskrit", "ś", "Shakya_clan", "ancient", "india", "buddhist_clan"), ("Saka", "persian", "s", "Scythian", "ancient", "central_asia", "nomadic_group"), ("Seljuk", "turkish", "s", "Seljuk_dynasty", "medieval", "turkey", "turkic_dynasty"), ("sabra", "sumerian", "s", "trade_official", "ancient", "mesopotamia", "administrative_title") ] return [SPhonemeWord(*data) for data in lexicon_data] def extract_s_phonemes(self, word: str) -> List[str]: """提取单词中的s音及其变体""" s_sounds = [] word_lower = word.lower() # 检查所有s音变体 for variant in self.s_variants.keys(): if variant in word_lower: # 找到所有出现的位置 positions = [m.start() for m in re.finditer(variant, word_lower)] for pos in positions: s_sounds.append({ 'variant': variant, 'position': pos, 'type': self.s_variants[variant] }) return s_sounds def calculate_phoneme_similarity(self, word1: str, word2: str) -> float: """计算两个单词的s音相似度""" # 提取s音部分 s1 = ''.join([c for c in word1.lower() if c in self.s_variants.keys()]) s2 = ''.join([c for c in word2.lower() if c in self.s_variants.keys()]) if not s1 or not s2: return 0.0 # 计算编辑距离相似度 distance = Levenshtein.distance(s1, s2) max_len = max(len(s1), len(s2)) return 1 - (distance / max_len) if max_len > 0 else 0.0 def analyze_s_phoneme_stability(self, word_list: List[str]) -> Dict: """分析s音在词汇列表中的稳定性""" total_words = len(word_list) s_phoneme_words = 0 s_phoneme_distribution = Counter() for word in word_list: s_phonemes = self.extract_s_phonemes(word) if s_phonemes: s_phoneme_words += 1 for phoneme in s_phonemes: s_phoneme_distribution[phoneme['variant']] += 1 stability_rate = s_phoneme_words / total_words if total_words > 0 else 0 return { 'total_words': total_words, 's_phoneme_words': s_phoneme_words, 'stability_rate': stability_rate, 'phoneme_distribution': dict(s_phoneme_distribution), 'most_common_phoneme': s_phoneme_distribution.most_common(1)[0] if s_phoneme_distribution else None } def find_cultural_transmission_paths(self, source_civ: str, target_civ: str) -> List[Dict]: """寻找文明间的s音传播路径""" paths = [] # 筛选相关文明的词汇 source_words = [w for w in self.silk_road_lexicon if w.language == source_civ] target_words = [w for w in self.silk_road_lexicon if w.language == target_civ] for s_word in source_words: for t_word in target_words: similarity = self.calculate_phoneme_similarity(s_word.word, t_word.word) if similarity > 0.3: # 相似度阈值 paths.append({ 'source_word': s_word.word, 'target_word': t_word.word, 'similarity': similarity, 'source_meaning': s_word.meaning, 'target_meaning': t_word.meaning, 'time_gap': self._estimate_time_gap(s_word.era, t_word.era) }) # 按相似度排序 paths.sort(key=lambda x: x['similarity'], reverse=True) return paths def _estimate_time_gap(self, era1: str, era2: str) -> str: """估算时间差距（简化版）""" era_order = { 'ancient': 1, 'classical': 2, 'medieval': 3, 'modern': 4 } order1 = era_order.get(era1, 0) order2 = era_order.get(era2, 0) gap = abs(order1 - order2) if gap == 0: return "contemporary" elif gap == 1: return "1_era_gap" else: return f"{gap}_eras_gap" def generate_cultural_gene_report(self) -> Dict: """生成文明基因分析报告""" # 分析s音稳定性 all_words = [w.word for w in self.silk_road_lexicon] stability_analysis = self.analyze_s_phoneme_stability(all_words) # 分析文明传播路径 transmission_paths = {} key_civilizations = ['sumerian', 'persian', 'sanskrit', 'chinese', 'turkish', 'english'] for i, civ1 in enumerate(key_civilizations): for civ2 in key_civilizations[i+1:]: path_key = f"{civ1}_to_{civ2}" paths = self.find_cultural_transmission_paths(civ1, civ2) if paths: transmission_paths[path_key] = { 'path_count': len(paths), 'strongest_connection': max(paths, key=lambda x: x['similarity']) if paths else None, 'avg_similarity': sum(p['similarity'] for p in paths) / len(paths) if paths else 0 } # 统计各文明的s音特征 civ_s_analysis = {} for civ in key_civilizations: civ_words = [w.word for w in self.silk_road_lexicon if w.language == civ] if civ_words: civ_s_analysis[civ] = self.analyze_s_phoneme_stability(civ_words) return { 'overall_stability': stability_analysis, 'transmission_paths': transmission_paths, 'civilization_analysis': civ_s_analysis, 'total_lexicon_size': len(self.silk_road_lexicon), 'key_findings': self._generate_key_findings(stability_analysis, transmission_paths) } def _generate_key_findings(self, stability: Dict, paths: Dict) -> List[str]: """生成关键发现""" findings = [] # s音稳定性发现 if stability['stability_rate'] > 0.8: findings.append(f"High_s_phoneme_stability:_{stability['stability_rate']:.1%}") # 传播路径发现 strong_paths = [p for p in paths.values() if p['avg_similarity'] > 0.5] if strong_paths: findings.append(f"Strong_cultural_transmission:_{len(strong_paths)}_paths") # 最稳定的s音变体 if stability['most_common_phoneme']: variant, count = stability['most_common_phoneme'] findings.append(f"Dominant_s_variant:_{variant}_({count}_occurrences)") return findings def main(): """主函数：演示s音文明基因分析""" print("🧬 s音文明基因验证工具包") print("=" * 50) # 初始化分析器 analyzer = SPhonemeAnalyzer() # 运行全面分析 report = analyzer.generate_cultural_gene_report() # 输出结果 print(f"📊 分析完成！词汇库规模: {report['total_lexicon_size']}") print(f"🎯 s音整体稳定性: {report['overall_stability']['stability_rate']:.2%}") print(f"🔗 发现传播路径: {len(report['transmission_paths'])}") print("\n🔍 关键发现:") for finding in report['key_findings']: print(f" • {finding.replace('_', ' ')}") # 详细分析示例 print("\n📈 文明间s音传播分析:") for path_key, data in list(report['transmission_paths'].items())[:3]: print(f"\n{path_key.replace('_', ' → ')}:") print(f" 路径数量: {data['path_count']}") print(f" 平均相似度: {data['avg_similarity']:.2f}") if data['strongest_connection']: conn = data['strongest_connection'] print(f" 最强连接: {conn['source_word']} → {conn['target_word']} (相似度: {conn['similarity']:.2f})") # 保存详细报告 output_file = "s_phoneme_civilization_report.json" with open(output_file, 'w', encoding='utf-8') as f: json.dump(report, f, ensure_ascii=False, indent=2, default=str) print(f"\n💾 详细报告已保存至: {output_file}") # 特定分析示例 print("\n🔬 特定词汇分析示例:") test_pairs = [ ("Śākya", "Saka"), ("丝", "silk"), ("商", "sart"), ("沙门", "śramaṇa") ] for word1, word2 in test_pairs: similarity = analyzer.calculate_phoneme_similarity(word1, word2) print(f" {word1} ↔ {word2}: 相似度 = {similarity:.3f}") if __name__ == "__main__": main()