import json
import os
import csv
from collections import defaultdict


def analyze_project_division(json_file_path):
    """
    分析JSON文件中的projectDivision数据，统计不同type节点的属性名

    Args:
        json_file_path: JSON文件路径

    Returns:
        dict: 按type分类的属性名集合
    """
    try:
        # 读取JSON文件
        with open(json_file_path, "r", encoding="utf-8") as f:
            data = json.load(f)

        # 检查是否存在projectData.projectDivision
        if "projectData" not in data or "projectDivision" not in data["projectData"]:
            print(f"文件 {json_file_path} 中不包含projectData.projectDivision数据")
            return {}

        project_division = data["projectData"]["projectDivision"]

        # 按type分类存储属性名
        type_attributes = defaultdict(set)

        # 递归遍历树状结构
        def traverse_node(node):
            if isinstance(node, dict):
                # 如果有type或类型字段，则使用该字段作为节点类型
                node_type = node.get("type", node.get("类型", "未知类型"))

                # 收集当前节点的所有属性名
                for attr_name in node.keys():
                    type_attributes[node_type].add(attr_name)

                # 处理子节点
                if "children" in node and isinstance(node["children"], list):
                    for child in node["children"]:
                        traverse_node(child)

                # 处理其他可能的嵌套结构
                for key, value in node.items():
                    if isinstance(value, dict) and key != "children":
                        traverse_node(value)
                    elif isinstance(value, list) and key != "children":
                        for item in value:
                            traverse_node(item)

            elif isinstance(node, list):
                for item in node:
                    traverse_node(item)

        # 处理projectDivision的每个顶级键
        for key, value in project_division.items():
            if isinstance(value, dict):
                traverse_node(value)
            elif isinstance(value, list):
                for item in value:
                    traverse_node(item)

        return type_attributes

    except Exception as e:
        print(f"处理文件 {json_file_path} 时出错: {e}")
        import traceback

        traceback.print_exc()
        return {}


def save_to_csv(type_attributes, output_file="node_attributes.csv"):
    """
    将统计结果保存到CSV文件

    Args:
        type_attributes: 按type分类的属性名集合
        output_file: 输出CSV文件名
    """
    with open(output_file, "w", encoding="utf-8", newline="") as f:
        writer = csv.writer(f)
        writer.writerow(["节点类型", "属性名"])

        for node_type, attributes in type_attributes.items():
            # 将属性名集合转换为排序后的列表
            sorted_attrs = sorted(attributes)
            for attr in sorted_attrs:
                writer.writerow([node_type, attr])


def main():
    """
    主函数
    """
    # 指定JSON文件路径，可以是单个文件或目录
    json_dir = "dataset/json/配网清单/2022行业招标3.1.12_readable.json"  # 可以根据实际情况修改

    # 存储所有文件的统计结果
    all_type_attributes = defaultdict(set)

    # 如果是目录，则遍历所有JSON文件
    if os.path.isdir(json_dir):
        for root, _, files in os.walk(json_dir):
            for file in files:
                if file.endswith(".json"):
                    file_path = os.path.join(root, file)
                    print(f"正在处理: {file_path}")

                    # 获取当前文件的统计结果
                    current_type_attrs = analyze_project_division(file_path)

                    # 合并结果
                    for node_type, attrs in current_type_attrs.items():
                        all_type_attributes[node_type].update(attrs)
    else:
        # 单个文件
        print(f"正在处理: {json_dir}")
        all_type_attributes = analyze_project_division(json_dir)

    # 保存结果到CSV
    save_to_csv(all_type_attributes)
    print(f"统计结果已保存到 node_attributes.csv")


if __name__ == "__main__":
    main()