492 lines
17 KiB
Python
492 lines
17 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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符号传承可视化系统
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胡汉三千年项目可视化工具
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功能：实现符号传播路径、阴阳对应关系、跨文明关联的可视化展示
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"""
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import json
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import sqlite3
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import matplotlib.pyplot as plt
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import networkx as nx
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from typing import Dict, List, Tuple
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from datetime import datetime
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import numpy as np
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from enum import Enum
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class VisualizationType(Enum):
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"""可视化类型枚举"""
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TRANSMISSION_MAP = "transmission_map" # 传播地图
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TIMELINE = "timeline" # 时间线
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YIN_YANG_MATRIX = "yin_yang_matrix" # 阴阳矩阵
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CIVILIZATION_NETWORK = "civilization_network" # 文明网络
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class SymbolVisualizer:
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"""符号可视化系统"""
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def __init__(self, db_path: str = "symbols.db"):
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"""初始化可视化系统"""
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self.db_path = db_path
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self.conn = sqlite3.connect(db_path)
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def plot_transmission_map(self, symbol_id: str, save_path: str = None):
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"""
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绘制符号传播地图
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Args:
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symbol_id: 符号ID
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save_path: 保存路径（可选）
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"""
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# 获取传播路径数据
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cursor = self.conn.cursor()
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cursor.execute("""
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WITH RECURSIVE symbol_path AS (
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SELECT s.symbol_id, s.origin_civilization, s.origin_period, 0 as depth
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FROM symbols s
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WHERE s.symbol_id = ?
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UNION ALL
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SELECT s.symbol_id, s.origin_civilization, s.origin_period, sp.depth + 1
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FROM symbols s
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JOIN cross_civilization_links l ON s.symbol_id = l.target_symbol_id
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JOIN symbol_path sp ON l.source_symbol_id = sp.symbol_id
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WHERE sp.depth < 10
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)
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SELECT * FROM symbol_path
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ORDER BY depth
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""", (symbol_id,))
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path_data = cursor.fetchall()
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if not path_data:
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print("未找到传播路径数据")
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return
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# 创建图形
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fig, ax = plt.subplots(figsize=(12, 8))
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# 定义文明位置（简化版世界地图）
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civilization_positions = {
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"Chinese": (1, 3), # 中国
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"Greek": (0, 2), # 希腊
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"Roman": (-1, 1), # 罗马
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"European": (-2, 0), # 欧洲
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"Global": (-3, -1) # 全球
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}
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# 绘制传播路径
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x_coords = []
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y_coords = []
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labels = []
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for i, (sym_id, civ, period, depth) in enumerate(path_data):
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if civ in civilization_positions:
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x, y = civilization_positions[civ]
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x_coords.append(x)
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y_coords.append(y)
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labels.append(f"{sym_id}\n({civ}, {period})")
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# 绘制连线
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ax.plot(x_coords, y_coords, 'o-', linewidth=2, markersize=8,
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markerfacecolor='red', markeredgecolor='black')
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# 添加标签
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for i, (x, y) in enumerate(zip(x_coords, y_coords)):
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ax.annotate(labels[i], (x, y), xytext=(5, 5),
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textcoords='offset points', fontsize=8)
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# 设置图形属性
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ax.set_title(f'符号传播路径：{symbol_id}', fontsize=14, fontweight='bold')
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ax.set_xlabel('文明传播方向（西→东）')
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ax.set_ylabel('时间维度')
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ax.grid(True, alpha=0.3)
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# 调整坐标轴范围
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ax.set_xlim(min(x_coords)-1, max(x_coords)+1)
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ax.set_ylim(min(y_coords)-1, max(y_coords)+1)
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"传播地图已保存至：{save_path}")
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plt.show()
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def plot_timeline(self, symbol_family: List[str], save_path: str = None):
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"""
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绘制符号家族时间线
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Args:
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symbol_family: 符号家族ID列表
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save_path: 保存路径（可选）
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"""
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cursor = self.conn.cursor()
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# 获取符号时间数据
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timeline_data = []
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for symbol_id in symbol_family:
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cursor.execute("""
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SELECT symbol_id, symbol_form, symbol_name, origin_civilization, origin_period
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FROM symbols
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WHERE symbol_id = ?
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""", (symbol_id,))
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result = cursor.fetchone()
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if result:
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timeline_data.append(result)
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if not timeline_data:
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print("未找到符号数据")
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return
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# 解析时间信息（简化处理）
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time_mapping = {
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"Zhou Dynasty": -1000, # 周朝
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"8th century BCE": -800, # 公元前8世纪
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"Roman": -200, # 罗马时期
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"European": 1000, # 欧洲中世纪
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"Global": 1500 # 大航海时代
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}
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# 创建时间线图
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fig, ax = plt.subplots(figsize=(14, 6))
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y_positions = []
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time_points = []
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labels = []
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for i, (sym_id, form, name, civ, period) in enumerate(timeline_data):
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time_point = time_mapping.get(civ, 0)
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time_points.append(time_point)
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y_positions.append(i)
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labels.append(f"{form} ({name})\n{civ}, {period}")
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# 绘制时间线
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ax.plot(time_points, y_positions, 'o-', linewidth=3, markersize=10,
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markerfacecolor='blue', markeredgecolor='black')
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# 添加标签
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for i, (time, y, label) in enumerate(zip(time_points, y_positions, labels)):
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ax.annotate(label, (time, y), xytext=(10, 0),
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textcoords='offset points', fontsize=9,
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bbox=dict(boxstyle="round,pad=0.3", facecolor="lightblue", alpha=0.7))
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# 设置图形属性
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ax.set_title('符号家族时间线', fontsize=14, fontweight='bold')
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ax.set_xlabel('时间（公元前→公元后）')
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ax.set_ylabel('符号序列')
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ax.grid(True, alpha=0.3)
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# 调整坐标轴
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ax.set_xlim(min(time_points)-500, max(time_points)+500)
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ax.set_ylim(-1, len(y_positions))
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"时间线图已保存至：{save_path}")
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plt.show()
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def plot_yin_yang_matrix(self, save_path: str = None):
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"""
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绘制阴阳符号矩阵
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Args:
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save_path: 保存路径（可选）
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"""
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cursor = self.conn.cursor()
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# 获取所有符号的阴阳属性
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cursor.execute("""
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SELECT symbol_id, symbol_form, yin_yang_attribute, engraving_type, origin_civilization
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FROM symbols
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ORDER BY origin_civilization, yin_yang_attribute
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""")
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symbols_data = cursor.fetchall()
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if not symbols_data:
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print("未找到符号数据")
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return
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# 创建阴阳矩阵
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civilizations = sorted(set([row[4] for row in symbols_data]))
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yin_yang_types = ['yin', 'yang', 'neutral']
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# 统计每个文明-阴阳组合的符号数量
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matrix_data = np.zeros((len(civilizations), len(yin_yang_types)))
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for row in symbols_data:
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sym_id, form, yin_yang, engraving, civ = row
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civ_index = civilizations.index(civ)
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yin_yang_index = yin_yang_types.index(yin_yang)
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matrix_data[civ_index, yin_yang_index] += 1
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# 创建热力图
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fig, ax = plt.subplots(figsize=(10, 8))
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im = ax.imshow(matrix_data, cmap='YlOrRd', aspect='auto')
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# 设置坐标轴标签
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ax.set_xticks(np.arange(len(yin_yang_types)))
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ax.set_yticks(np.arange(len(civilizations)))
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ax.set_xticklabels(yin_yang_types)
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ax.set_yticklabels(civilizations)
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# 添加数值标签
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for i in range(len(civilizations)):
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for j in range(len(yin_yang_types)):
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text = ax.text(j, i, int(matrix_data[i, j]),
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ha="center", va="center", color="black", fontweight='bold')
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# 设置图形属性
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ax.set_title('阴阳符号分布矩阵', fontsize=14, fontweight='bold')
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ax.set_xlabel('阴阳属性')
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ax.set_ylabel('文明')
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# 添加颜色条
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cbar = ax.figure.colorbar(im, ax=ax)
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cbar.ax.set_ylabel('符号数量', rotation=-90, va="bottom")
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"阴阳矩阵图已保存至：{save_path}")
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plt.show()
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def plot_civilization_network(self, save_path: str = None):
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"""
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绘制文明网络图
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Args:
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save_path: 保存路径（可选）
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"""
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cursor = self.conn.cursor()
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# 获取文明关联数据
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cursor.execute("""
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SELECT DISTINCT s1.origin_civilization, s2.origin_civilization, COUNT(*) as link_count
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FROM cross_civilization_links l
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JOIN symbols s1 ON l.source_symbol_id = s1.symbol_id
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JOIN symbols s2 ON l.target_symbol_id = s2.symbol_id
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WHERE s1.origin_civilization != s2.origin_civilization
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GROUP BY s1.origin_civilization, s2.origin_civilization
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HAVING link_count > 0
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""")
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network_data = cursor.fetchall()
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if not network_data:
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print("未找到文明关联数据")
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return
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# 创建网络图
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G = nx.Graph()
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# 添加节点和边
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for source_civ, target_civ, weight in network_data:
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G.add_edge(source_civ, target_civ, weight=weight)
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# 设置节点位置
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pos = nx.spring_layout(G, k=1, iterations=50)
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# 绘制网络图
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fig, ax = plt.subplots(figsize=(12, 10))
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# 计算节点大小（基于连接数量）
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node_sizes = [G.degree(node) * 500 for node in G.nodes()]
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# 绘制边
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edge_widths = [G[u][v]['weight'] for u, v in G.edges()]
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nx.draw_networkx_edges(G, pos, alpha=0.5, width=edge_widths,
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edge_color='gray')
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# 绘制节点
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nx.draw_networkx_nodes(G, pos, node_size=node_sizes,
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node_color='lightblue', alpha=0.9,
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edgecolors='black')
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# 绘制标签
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nx.draw_networkx_labels(G, pos, font_size=10, font_weight='bold')
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# 设置图形属性
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ax.set_title('文明符号传播网络', fontsize=16, fontweight='bold')
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ax.axis('off')
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# 添加图例
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ax.text(0.02, 0.98, '节点大小：连接数量\n边宽度：关联强度',
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transform=ax.transAxes, fontsize=10,
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bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8))
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if save_path:
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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print(f"文明网络图已保存至：{save_path}")
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plt.show()
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def create_comparison_chart(self, symbol_list: List[str], save_path: str = None):
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"""
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创建符号对比图表
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Args:
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symbol_list: 符号ID列表
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save_path: 保存路径（可选）
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"""
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cursor = self.conn.cursor()
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# 获取符号对比数据
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comparison_data = []
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for symbol_id in symbol_list:
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cursor.execute("""
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SELECT symbol_id, symbol_form, symbol_name, yin_yang_attribute,
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engraving_type, origin_civilization, origin_period
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FROM symbols
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WHERE symbol_id = ?
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""", (symbol_id,))
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result = cursor.fetchone()
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if result:
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comparison_data.append(result)
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if not comparison_data:
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print("未找到符号数据")
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return
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# 创建对比图表
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fig, axes = plt.subplots(2, 2, figsize=(15, 12))
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fig.suptitle('符号对比分析', fontsize=16, fontweight='bold')
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# 1. 阴阳属性分布
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yin_yang_counts = {'yin': 0, 'yang': 0, 'neutral': 0}
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for data in comparison_data:
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yin_yang = data[3]
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yin_yang_counts[yin_yang] += 1
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axes[0, 0].pie(yin_yang_counts.values(), labels=yin_yang_counts.keys(),
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autopct='%1.1f%%', startangle=90)
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axes[0, 0].set_title('阴阳属性分布')
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# 2. 刻法类型分布
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engraving_counts = {'yin_engraving': 0, 'yang_engraving': 0, 'mixed': 0}
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for data in comparison_data:
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engraving = data[4]
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engraving_counts[engraving] += 1
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axes[0, 1].bar(engraving_counts.keys(), engraving_counts.values(),
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color=['lightcoral', 'lightblue', 'lightgreen'])
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axes[0, 1].set_title('刻法类型分布')
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axes[0, 1].tick_params(axis='x', rotation=45)
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# 3. 文明分布
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civilization_counts = {}
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for data in comparison_data:
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civ = data[5]
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civilization_counts[civ] = civilization_counts.get(civ, 0) + 1
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axes[1, 0].barh(list(civilization_counts.keys()), list(civilization_counts.values()),
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color='lightsteelblue')
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axes[1, 0].set_title('文明分布')
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# 4. 符号形态展示
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symbol_forms = [data[1] for data in comparison_data]
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symbol_names = [data[2] for data in comparison_data]
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axes[1, 1].text(0.5, 0.5, '\n'.join([f"{form}: {name}" for form, name in zip(symbol_forms, symbol_names)]),
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ha='center', va='center', transform=axes[1, 1].transAxes,
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fontsize=12, bbox=dict(boxstyle="round,pad=0.5", facecolor="wheat", alpha=0.5))
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axes[1, 1].set_title('符号形态列表')
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axes[1, 1].axis('off')
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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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print(f"对比图表已保存至：{save_path}")
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plt.show()
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def export_visualization_report(self, symbol_id: str, output_dir: str = "./reports"):
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"""
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导出完整的可视化报告
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Args:
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symbol_id: 符号ID
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output_dir: 输出目录
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"""
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import os
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os.makedirs(output_dir, exist_ok=True)
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# 生成各种可视化图表
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visualizations = [
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("transmission_map", self.plot_transmission_map),
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("yin_yang_matrix", self.plot_yin_yang_matrix),
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("civilization_network", self.plot_civilization_network)
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]
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generated_files = []
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for viz_name, viz_func in visualizations:
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file_path = os.path.join(output_dir, f"{symbol_id}_{viz_name}.png")
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try:
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if viz_name == "transmission_map":
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viz_func(symbol_id, file_path)
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else:
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viz_func(file_path)
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generated_files.append(file_path)
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print(f"生成图表：{file_path}")
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except Exception as e:
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print(f"生成 {viz_name} 时出错：{e}")
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# 创建报告文件
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report_path = os.path.join(output_dir, f"{symbol_id}_visualization_report.md")
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write(f"# 符号可视化报告：{symbol_id}\n\n")
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f.write(f"生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
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for file_path in generated_files:
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filename = os.path.basename(file_path)
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f.write(f"## {filename.replace('_', ' ').replace('.png', '')}\n\n")
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f.write(f"\n\n")
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print(f"可视化报告已生成：{report_path}")
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return generated_files
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# 使用示例
|
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def main():
|
||
"""主函数示例"""
|
||
# 创建可视化系统
|
||
visualizer = SymbolVisualizer()
|
||
|
||
# 示例1：绘制P符号传播地图
|
||
print("=== 绘制P符号传播地图 ===")
|
||
visualizer.plot_transmission_map("P_yin_001")
|
||
|
||
# 示例2：绘制阴阳符号矩阵
|
||
print("=== 绘制阴阳符号矩阵 ===")
|
||
visualizer.plot_yin_yang_matrix()
|
||
|
||
# 示例3：绘制文明网络图
|
||
print("=== 绘制文明网络图 ===")
|
||
visualizer.plot_civilization_network()
|
||
|
||
# 示例4：创建符号对比图表
|
||
print("=== 创建符号对比图表 ===")
|
||
symbol_list = ["P_yin_001", "T_yang_001", "Pi_yin_001", "Tau_yang_001"]
|
||
visualizer.create_comparison_chart(symbol_list)
|
||
|
||
# 示例5：导出完整可视化报告
|
||
print("=== 导出完整可视化报告 ===")
|
||
visualizer.export_visualization_report("P_yin_001")
|
||
|
||
if __name__ == "__main__":
|
||
main() |