#!/usr/bin/env python3 """ 8维全息扫描：360度人格评估 """ import json import numpy as np from pathlib import Path from typing import Dict, List class HolographicScanner: """8维全息扫描器""" # 8个维度定义 DIMENSIONS = [ 'cognitive', # 认知维度 'emotional', # 情感维度 'behavioral', # 行为维度 'social', # 社交维度 'creative', # 创造维度 'spiritual', # 灵性维度 'practical', # 实践维度 'shadow' # 阴影维度 ] def __init__(self): self.scan_results = {} def scan_profile(self, profile_data: Dict, zodiac_sign: str) -> Dict: """执行全息扫描""" scan_result = { 'zodiac_sign': zodiac_sign, 'timestamp': str(np.datetime64('now')), 'dimensions': {} } # 扫描每个维度 for dimension in self.DIMENSIONS: scan_result['dimensions'][dimension] = self._scan_dimension( dimension, profile_data, zodiac_sign ) # 计算总体特征 scan_result['overall_signature'] = self._calculate_overall_signature( scan_result['dimensions'] ) # 识别矛盾模式 scan_result['paradox_patterns'] = self._identify_paradoxes( scan_result['dimensions'] ) # 识别核心优势 scan_result['core_strengths'] = self._identify_strengths( scan_result['dimensions'] ) # 识别潜在盲区 scan_result['blind_spots'] = self._identify_blind_spots( scan_result['dimensions'] ) return scan_result def _scan_dimension(self, dimension: str, profile_data: Dict, zodiac_sign: str) -> Dict: """扫描单个维度""" # 模拟扫描逻辑（实际需要更复杂的分析） scores = self._generate_dimension_scores(dimension, zodiac_sign) return { 'dimension': dimension, 'scores': scores, 'primary_traits': self._extract_primary_traits(dimension, scores), 'developmental_level': self._assess_developmental_level(scores), 'integration_degree': self._assess_integration_degree(scores) } def _generate_dimension_scores(self, dimension: str, zodiac_sign: str) -> Dict: """生成维度分数（模拟）""" # 使用随机种子确保相同输入产生相同输出 np.random.seed(hash(f"{dimension}_{zodiac_sign}") % 2**32) scores = { 'raw_score': np.random.uniform(5, 10), 'normalized_score': 0, # 稍后计算 'stability': np.random.uniform(0.6, 0.95), 'flexibility': np.random.uniform(0.5, 0.9) } # 归一化到0-1 scores['normalized_score'] = (scores['raw_score'] - 5) / 5 return scores def _extract_primary_traits(self, dimension: str, scores: Dict) -> List[str]: """提取主要特征""" trait_libraries = { 'cognitive': ['逻辑思维', '直觉感知', '分析能力', '综合整合'], 'emotional': ['情感深度', '情感表达', '情绪调节', '共情能力'], 'behavioral': ['行动力', '执行力', '适应性', '稳定性'], 'social': ['人际敏感', '沟通表达', '团队协作', '领导能力'], 'creative': ['想象力', '创新性', '原创性', '艺术性'], 'spiritual': ['意义感', '超越性', '整合性', '直觉'], 'practical': ['实用性', '效率', '规划能力', '执行细节'], 'shadow': ['潜在恐惧', '防御机制', '未整合部分', '矛盾冲突'] } traits = trait_libraries.get(dimension, []) # 选择前2-3个 return traits[:2] def _assess_developmental_level(self, scores: Dict) -> str: """评估发展水平""" raw_score = scores['raw_score'] if raw_score >= 9: return 'excellent' elif raw_score >= 8: return 'advanced' elif raw_score >= 7: return 'proficient' elif raw_score >= 6: return 'developing' else: return 'emerging' def _assess_integration_degree(self, scores: Dict) -> float: """评估整合程度""" # 结合稳定性和灵活性 stability = scores['stability'] flexibility = scores['flexibility'] return (stability + flexibility) / 2 def _calculate_overall_signature(self, dimensions: Dict) -> Dict: """计算总体特征""" # 收集所有维度的分数 raw_scores = [ dim_info['scores']['raw_score'] for dim_info in dimensions.values() ] # 计算统计特征 signature = { 'mean': np.mean(raw_scores), 'std': np.std(raw_scores), 'max': np.max(raw_scores), 'min': np.min(raw_scores), 'range': np.ptp(raw_scores) } # 计算综合得分 signature['composite_score'] = np.mean([ dim_info['scores']['normalized_score'] for dim_info in dimensions.values() ]) # 确定主要类型 signature['dominant_dimensions'] = self._identify_dominant_dimensions( dimensions ) return signature def _identify_dominant_dimensions(self, dimensions: Dict) -> List[str]: """识别主导维度""" sorted_dims = sorted( dimensions.items(), key=lambda x: x[1]['scores']['raw_score'], reverse=True ) return [dim_name for dim_name, _ in sorted_dims[:3]] def _identify_paradoxes(self, dimensions: Dict) -> List[Dict]: """识别矛盾模式""" paradoxes = [] # 检查对立维度的张力 paradox_pairs = [ ('cognitive', 'emotional'), ('creative', 'practical'), ('social', 'shadow'), ('spiritual', 'practical') ] for dim1, dim2 in paradox_pairs: if dim1 in dimensions and dim2 in dimensions: score1 = dimensions[dim1]['scores']['raw_score'] score2 = dimensions[dim2]['scores']['raw_score'] tension = abs(score1 - score2) if tension > 3: paradoxes.append({ 'type': f"{dim1}_vs_{dim2}", 'tension': tension, 'higher_dimension': dim1 if score1 > score2 else dim2, 'lower_dimension': dim1 if score1 < score2 else dim2, 'description': f"{dim1}与{dim2}之间存在显著张力" }) return paradoxes def _identify_strengths(self, dimensions: Dict) -> List[Dict]: """识别核心优势""" strengths = [] for dim_name, dim_info in dimensions.items(): raw_score = dim_info['scores']['raw_score'] if raw_score >= 8: strengths.append({ 'dimension': dim_name, 'score': raw_score, 'level': dim_info['developmental_level'], 'primary_traits': dim_info['primary_traits'] }) return strengths def _identify_blind_spots(self, dimensions: Dict) -> List[Dict]: """识别潜在盲区""" blind_spots = [] for dim_name, dim_info in dimensions.items(): raw_score = dim_info['scores']['raw_score'] if raw_score <= 6: blind_spots.append({ 'dimension': dim_name, 'score': raw_score, 'level': dim_info['developmental_level'], 'suggestion': f"需要强化{dim_name}维度的能力" }) return blind_spots def main(): import argparse parser = argparse.ArgumentParser(description='8维全息扫描') parser.add_argument('--profile', type=str, required=True, help='配置文件路径 (JSON)') parser.add_argument('--zodiac', type=str, required=True, help='星座名称') args = parser.parse_args() # 读取配置文件 profile_path = Path(args.profile) with open(profile_path, 'r', encoding='utf-8') as f: profile_data = json.load(f) # 执行扫描 scanner = HolographicScanner() result = scanner.scan_profile(profile_data, args.zodiac) print(json.dumps(result, ensure_ascii=False, indent=2)) if __name__ == '__main__': main()