463 lines
19 KiB
Python
463 lines
19 KiB
Python
"""
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生育焦虑量化分析系统
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分析拓跋鲜卑皇室的生育焦虑程度及其与政治政策、宗教活动的关联
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"""
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import statistics
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import numpy as np
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import matplotlib.pyplot as plt
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from typing import List, Dict, Any, Tuple
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import pandas as pd
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from collections import Counter
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from dataclasses import asdict
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from analysis.models import Emperor, ReliabilityLevel
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from data.emperors.northern_wei_emperors import NORTHERN_WEI_EMPERORS
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class FertilityAnxietyAnalyzer:
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"""生育焦虑量化分析器"""
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def __init__(self, emperors: List[Emperor] = None):
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self.emperors = emperors or NORTHERN_WEI_EMPERORS
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# 生育焦虑评估标准
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self.anxiety_indicators = {
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"low_offspring": 0.3, # 子嗣少于平均值
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"short_lifespan": 0.2, # 短寿
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"religious_activity": 0.2, # 频繁宗教活动
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"violent_death": 0.2, # 非自然死亡
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"early_succession": 0.1 # 早期传位
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}
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# 宗教活动关键词
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self.religious_keywords = [
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"祭祀", "祈福", "天师", "道教", "佛教", "寺庙",
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"昊天", "上帝", "神", "祖先", "宗庙", "太庙"
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]
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# 生育相关宗教活动关键词
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self.fertility_religious_keywords = [
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"祈子", "求嗣", "生育", "多子", "繁衍", "子孙", "后代"
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]
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def calculate_fertility_anxiety_score(self, emperor: Emperor) -> float:
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"""计算单个皇帝的生育焦虑评分"""
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if emperor.fertility_anxiety_score is not None:
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return emperor.fertility_anxiety_score
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score = 0.0
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# 1. 子嗣数量因子
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if emperor.offspring_count is not None:
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avg_offspring = self._get_average_offspring_count()
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if emperor.offspring_count < avg_offspring:
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score += self.anxiety_indicators["low_offspring"]
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# 2. 寿命因子
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if emperor.lifespan is not None and emperor.lifespan < 30:
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score += self.anxiety_indicators["short_lifespan"]
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# 3. 宗教活动因子
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religious_score = self._analyze_religious_activities(emperor.religious_activities)
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score += religious_score * self.anxiety_indicators["religious_activity"]
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# 4. 死因因子
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if emperor.death_cause and any(keyword in emperor.death_cause
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for keyword in ["杀", "害", "毒", "暗杀"]):
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score += self.anxiety_indicators["violent_death"]
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# 5. 在位时长因子（早期传位可能表示焦虑）
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if emperor.reign_duration is not None and emperor.reign_duration < 10:
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score += self.anxiety_indicators["early_succession"]
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return min(score, 1.0) # 限制在0-1之间
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def _get_average_offspring_count(self) -> float:
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"""获取平均子嗣数量"""
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offspring_counts = [emp.offspring_count for emp in self.emperors
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if emp.offspring_count is not None]
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return statistics.mean(offspring_counts) if offspring_counts else 5.0
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def _analyze_religious_activities(self, activities: List[str]) -> float:
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"""分析宗教活动的生育焦虑相关性"""
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if not activities:
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return 0.0
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total_score = 0.0
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activity_text = " ".join(activities)
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# 检查一般宗教活动
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religious_count = sum(1 for keyword in self.religious_keywords
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if keyword in activity_text)
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# 检查生育相关宗教活动（权重更高）
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fertility_religious_count = sum(1 for keyword in self.fertility_religious_keywords
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if keyword in activity_text)
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# 计算评分
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total_score = (religious_count * 0.1 + fertility_religious_count * 0.3) / len(activities)
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return min(total_score, 1.0)
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def analyze_anxiety_distribution(self) -> Dict[str, Any]:
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"""分析生育焦虑分布"""
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anxiety_scores = []
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emperors_with_scores = []
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for emperor in self.emperors:
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score = self.calculate_fertility_anxiety_score(emperor)
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anxiety_scores.append(score)
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emperors_with_scores.append((emperor, score))
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if not anxiety_scores:
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return {"error": "无法计算生育焦虑评分"}
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# 按焦虑程度分类
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high_anxiety = [emp for emp, score in emperors_with_scores if score >= 0.7]
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medium_anxiety = [emp for emp, score in emperors_with_scores if 0.4 <= score < 0.7]
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low_anxiety = [emp for emp, score in emperors_with_scores if score < 0.4]
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return {
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"total_emperors": len(self.emperors),
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"mean_anxiety": statistics.mean(anxiety_scores),
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"median_anxiety": statistics.median(anxiety_scores),
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"std_anxiety": statistics.stdev(anxiety_scores) if len(anxiety_scores) > 1 else 0,
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"high_anxiety_count": len(high_anxiety),
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"medium_anxiety_count": len(medium_anxiety),
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"low_anxiety_count": len(low_anxiety),
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"high_anxiety_emperors": [emp.name for emp in high_anxiety],
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"anxiety_scores": dict(zip([emp.name for emp in self.emperors], anxiety_scores))
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}
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def analyze_anxiety_policy_correlation(self) -> Dict[str, Any]:
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"""分析生育焦虑与政策变化的相关性"""
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policy_changes = []
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anxiety_levels = []
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for emperor in self.emperors:
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anxiety_score = self.calculate_fertility_anxiety_score(emperor)
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# 分析政策变化指标
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policy_score = self._calculate_policy_change_score(emperor)
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if policy_score is not None:
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anxiety_levels.append(anxiety_score)
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policy_changes.append(policy_score)
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if len(anxiety_levels) < 3:
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return {"error": "数据不足，无法进行相关性分析"}
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correlation = self._calculate_correlation(anxiety_levels, policy_changes)
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return {
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"sample_size": len(anxiety_levels),
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"correlation": correlation,
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"interpretation": self._interpret_policy_correlation(correlation)
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}
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def _calculate_policy_change_score(self, emperor: Emperor) -> float:
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"""计算政策变化评分"""
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score = 0.0
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# 宗教政策变化
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religious_activities = emperor.religious_activities or []
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activity_text = " ".join(religious_activities)
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# 重大宗教政策变化关键词
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major_changes = ["改革", "迁都", "灭佛", "复兴", "建立", "废除"]
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change_count = sum(1 for keyword in major_changes if keyword in activity_text)
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score += change_count * 0.2
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# 在位时长（可能反映政策稳定性）
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if emperor.reign_duration is not None:
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if emperor.reign_duration > 20:
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score += 0.3 # 长期在位，政策相对稳定
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elif emperor.reign_duration < 5:
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score += 0.1 # 短期在位，政策变化有限
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return min(score, 1.0)
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def analyze_anxiety_religious_correlation(self) -> Dict[str, Any]:
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"""分析生育焦虑与宗教活动频率的关联"""
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anxiety_scores = []
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religious_frequencies = []
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for emperor in self.emperors:
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anxiety_score = self.calculate_fertility_anxiety_score(emperor)
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religious_freq = len(emperor.religious_activities or [])
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anxiety_scores.append(anxiety_score)
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religious_frequencies.append(religious_freq)
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if len(anxiety_scores) < 3:
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return {"error": "数据不足"}
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correlation = self._calculate_correlation(anxiety_scores, religious_frequencies)
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# 分析特定类型的宗教活动
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fertility_religious_analysis = self._analyze_fertility_religious_activities()
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return {
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"sample_size": len(anxiety_scores),
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"anxiety_religious_correlation": correlation,
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"fertility_religious_analysis": fertility_religious_analysis,
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"interpretation": self._interpret_religious_correlation(correlation)
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}
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def _analyze_fertility_religious_activities(self) -> Dict[str, Any]:
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"""分析生育相关宗教活动"""
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fertility_activities = []
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for emperor in self.emperors:
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activities = emperor.religious_activities or []
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activity_text = " ".join(activities)
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fertility_count = sum(1 for keyword in self.fertility_religious_keywords
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if keyword in activity_text)
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if fertility_count > 0:
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fertility_activities.append({
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"emperor": emperor.name,
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"anxiety_score": self.calculate_fertility_anxiety_score(emperor),
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"fertility_activities": fertility_count,
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"total_activities": len(activities)
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})
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return {
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"emperors_with_fertility_activities": len(fertility_activities),
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"fertility_activities_details": fertility_activities
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}
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def _calculate_correlation(self, x: List[float], y: List[float]) -> Dict[str, float]:
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"""计算皮尔逊相关系数"""
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if len(x) != len(y) or len(x) < 2:
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return {"correlation": 0.0, "p_value": 1.0}
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n = len(x)
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sum_x = sum(x)
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sum_y = sum(y)
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sum_xy = sum(xi * yi for xi, yi in zip(x, y))
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sum_x2 = sum(xi * xi for xi in x)
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sum_y2 = sum(yi * yi for yi in y)
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numerator = n * sum_xy - sum_x * sum_y
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denominator = ((n * sum_x2 - sum_x * sum_x) * (n * sum_y2 - sum_y * sum_y)) ** 0.5
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if denominator == 0:
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correlation = 0.0
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else:
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correlation = numerator / denominator
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# 简化的p值估算
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t_stat = correlation * ((n - 2) / (1 - correlation**2)) ** 0.5 if abs(correlation) != 1 else float('inf')
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p_value = 2 * (1 - abs(t_stat) / (abs(t_stat) + n - 2)) if t_stat != float('inf') else 0.0
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return {
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"correlation": correlation,
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"p_value": p_value,
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"sample_size": n
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}
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def _interpret_policy_correlation(self, correlation: Dict[str, float]) -> str:
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"""解释政策相关性"""
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corr = correlation["correlation"]
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p_val = correlation["p_value"]
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if abs(corr) >= 0.5 and p_val < 0.05:
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direction = "正" if corr > 0 else "负"
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return f"生育焦虑与政策变化呈{direction}相关，相关性较强且统计显著"
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elif abs(corr) >= 0.3:
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direction = "正" if corr > 0 else "负"
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return f"生育焦虑与政策变化呈{direction}相关，相关性中等"
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else:
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return "生育焦虑与政策变化相关性较弱"
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def _interpret_religious_correlation(self, correlation: Dict[str, float]) -> str:
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"""解释宗教相关性"""
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corr = correlation["correlation"]
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p_val = correlation["p_value"]
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if corr >= 0.3 and p_val < 0.05:
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return "生育焦虑与宗教活动频率呈正相关，支持'焦虑驱动宗教活动'假说"
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elif corr >= 0.1:
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return "生育焦虑与宗教活动频率呈弱正相关"
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else:
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return "生育焦虑与宗教活动频率相关性不明显"
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def generate_anxiety_visualization(self, save_path: str = None) -> str:
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"""生成生育焦虑可视化图表"""
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS']
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plt.rcParams['axes.unicode_minus'] = False
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
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# 计算所有皇帝的焦虑评分
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emperors_scores = [(emp, self.calculate_fertility_anxiety_score(emp))
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for emp in self.emperors]
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names = [emp.name.split('拓跋')[0] for emp, _ in emperors_scores]
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anxiety_scores = [score for _, score in emperors_scores]
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# 1. 生育焦虑评分条形图
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colors = ['red' if score >= 0.7 else 'orange' if score >= 0.4 else 'green'
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for score in anxiety_scores]
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bars = ax1.bar(range(len(names)), anxiety_scores, color=colors, alpha=0.7)
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ax1.set_xlabel('皇帝')
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ax1.set_ylabel('生育焦虑评分')
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ax1.set_title('北魏皇帝生育焦虑评分')
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ax1.set_xticks(range(len(names)))
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ax1.set_xticklabels(names, rotation=45, ha='right')
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ax1.axhline(0.7, color='red', linestyle='--', alpha=0.5, label='高焦虑阈值')
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ax1.axhline(0.4, color='orange', linestyle='--', alpha=0.5, label='中焦虑阈值')
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ax1.legend()
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# 添加数值标签
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for bar, score in zip(bars, anxiety_scores):
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ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,
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f'{score:.2f}', ha='center', va='bottom', fontsize=8)
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# 2. 焦虑评分分布直方图
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ax2.hist(anxiety_scores, bins=10, alpha=0.7, color='lightblue', edgecolor='black')
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ax2.axvline(statistics.mean(anxiety_scores), color='red', linestyle='--',
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label=f'平均焦虑: {statistics.mean(anxiety_scores):.2f}')
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ax2.set_xlabel('生育焦虑评分')
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ax2.set_ylabel('频数')
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ax2.set_title('生育焦虑评分分布')
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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# 3. 焦虑与子嗣数量关系
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offspring_data = [(self.calculate_fertility_anxiety_score(emp), emp.offspring_count)
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for emp in self.emperors if emp.offspring_count is not None]
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if offspring_data:
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anxiety_vals, offspring_vals = zip(*offspring_data)
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ax3.scatter(anxiety_vals, offspring_vals, alpha=0.7, s=60)
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# 添加趋势线
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z = np.polyfit(anxiety_vals, offspring_vals, 1)
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p = np.poly1d(z)
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ax3.plot(anxiety_vals, p(anxiety_vals), "r--", alpha=0.8)
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ax3.set_xlabel('生育焦虑评分')
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ax3.set_ylabel('子嗣数量')
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ax3.set_title('生育焦虑与子嗣数量关系')
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ax3.grid(True, alpha=0.3)
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# 4. 焦虑与宗教活动关系
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religious_data = [(self.calculate_fertility_anxiety_score(emp),
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len(emp.religious_activities or []))
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for emp in self.emperors]
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if religious_data:
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anxiety_vals, religious_vals = zip(*religious_data)
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ax4.scatter(anxiety_vals, religious_vals, alpha=0.7, s=60, color='purple')
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# 添加趋势线
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z = np.polyfit(anxiety_vals, religious_vals, 1)
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p = np.poly1d(z)
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ax4.plot(anxiety_vals, p(anxiety_vals), "r--", alpha=0.8)
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ax4.set_xlabel('生育焦虑评分')
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ax4.set_ylabel('宗教活动数量')
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ax4.set_title('生育焦虑与宗教活动关系')
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ax4.grid(True, alpha=0.3)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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return f"图表已保存到: {save_path}"
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else:
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plt.show()
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return "图表已显示"
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def generate_comprehensive_anxiety_report(self) -> Dict[str, Any]:
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"""生成综合生育焦虑分析报告"""
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report = {
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"analysis_date": pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"),
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"methodology": {
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"anxiety_indicators": self.anxiety_indicators,
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"religious_keywords": len(self.religious_keywords),
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"fertility_keywords": len(self.fertility_religious_keywords)
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}
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}
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# 焦虑分布分析
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report["anxiety_distribution"] = self.analyze_anxiety_distribution()
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# 政策相关性分析
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report["policy_correlation"] = self.analyze_anxiety_policy_correlation()
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# 宗教活动相关性分析
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report["religious_correlation"] = self.analyze_anxiety_religious_correlation()
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# 关键发现
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report["key_findings"] = self._extract_anxiety_findings(report)
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return report
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def _extract_anxiety_findings(self, report: Dict[str, Any]) -> List[str]:
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"""提取生育焦虑关键发现"""
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findings = []
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# 焦虑水平发现
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if "mean_anxiety" in report["anxiety_distribution"]:
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mean_anxiety = report["anxiety_distribution"]["mean_anxiety"]
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findings.append(f"北魏皇室平均生育焦虑评分为 {mean_anxiety:.2f}，显示中等偏高的焦虑水平")
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# 高焦虑皇帝发现
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if "high_anxiety_count" in report["anxiety_distribution"]:
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high_count = report["anxiety_distribution"]["high_anxiety_count"]
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total_count = report["anxiety_distribution"]["total_emperors"]
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findings.append(f"{high_count}/{total_count} 位皇帝表现出高度生育焦虑")
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# 宗教活动相关性发现
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if "anxiety_religious_correlation" in report["religious_correlation"]:
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corr_data = report["religious_correlation"]["anxiety_religious_correlation"]
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if corr_data["correlation"] > 0.3:
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findings.append(f"生育焦虑与宗教活动呈正相关 (r={corr_data['correlation']:.3f})，支持宗教缓解焦虑假说")
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return findings
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|
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# 创建分析器实例
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fertility_analyzer = FertilityAnxietyAnalyzer()
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|
|
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def run_fertility_anxiety_analysis():
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"""运行生育焦虑分析"""
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print("开始北魏皇室生育焦虑量化分析...")
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|
|
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# 生成综合报告
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report = fertility_analyzer.generate_comprehensive_anxiety_report()
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|
|
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print("\n=== 北魏皇室生育焦虑分析报告 ===")
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print(f"分析时间: {report['analysis_date']}")
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|
|
|
# 焦虑分布
|
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dist = report['anxiety_distribution']
|
|
if 'error' not in dist:
|
|
print(f"\n平均生育焦虑评分: {dist['mean_anxiety']:.3f}")
|
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print(f"高焦虑皇帝: {dist['high_anxiety_count']}/{dist['total_emperors']}")
|
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print(f"高焦虑皇帝名单: {', '.join(dist['high_anxiety_emperors'])}")
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|
|
|
# 相关性分析
|
|
if 'error' not in report['religious_correlation']:
|
|
rel_corr = report['religious_correlation']['anxiety_religious_correlation']
|
|
print(f"\n生育焦虑与宗教活动相关性: {rel_corr['correlation']:.3f}")
|
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print(f"解释: {report['religious_correlation']['interpretation']}")
|
|
|
|
# 关键发现
|
|
print("\n=== 关键发现 ===")
|
|
for i, finding in enumerate(report['key_findings'], 1):
|
|
print(f"{i}. {finding}")
|
|
|
|
return report
|
|
|
|
if __name__ == "__main__":
|
|
report = run_fertility_anxiety_analysis()
|
|
|
|
# 生成可视化图表
|
|
chart_result = fertility_analyzer.generate_anxiety_visualization("fertility_anxiety_analysis.png")
|
|
print(f"\n{chart_result}") |