Agent/examples_how/overall_derivation/generate_visualize_data.py

#!/usr/bin/env python3
"""
生成推导可视化数据。

输入参数：account_name, post_id, log_id
- 从 input/{account_name}/解构内容/{post_id}.json 解析选题点列表
- 从 output/{account_name}/推导日志/{post_id}/{log_id}/ 读取推导与评估 JSON，生成：
  1. output/{account_name}/整体推导结果/{post_id}.json
  2. output/{account_name}/整体推导路径可视化/{post_id}.json
"""

import argparse
import json
import re
from pathlib import Path
from typing import Any, Dict, List, Optional


def _walk_tree_children_for_persona(
    children: Any, persona_by_name: Dict[str, Dict[str, Any]]
) -> None:
    """递归遍历人设树 children，按节点名（与 input_tree_nodes 短名一致）登记 type / 常量标记。"""
    if not isinstance(children, dict):
        return
    for name, node in children.items():
        if not isinstance(node, dict):
            continue
        if name not in persona_by_name:
            persona_by_name[name] = {
                "name": name,
                "type": node.get("_type"),
                "is_constant": bool(node.get("_is_constant", False)),
                "is_local_constant": bool(node.get("_is_local_constant", False)),
            }
        sub = node.get("children")
        if isinstance(sub, dict):
            _walk_tree_children_for_persona(sub, persona_by_name)


def build_persona_by_name_from_tree_dir(tree_dir: Path) -> Dict[str, Dict[str, Any]]:
    """
    从 input/{account}/处理后数据/tree 下所有人设树 JSON（如 *_point_tree_how.json）构建 name -> 人设节点信息。
    同名节点以首次出现为准，与 process_pipeline_tree_data.build_persona_by_name 用法一致。
    """
    persona_by_name: Dict[str, Dict[str, Any]] = {}
    if not tree_dir.is_dir():
        return persona_by_name
    for path in sorted(tree_dir.glob("*_point_tree_how.json")):
        with open(path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if not isinstance(data, dict):
            continue
        for _dim, root in data.items():
            if not isinstance(root, dict):
                continue
            ch = root.get("children")
            _walk_tree_children_for_persona(ch, persona_by_name)
    return persona_by_name


def _node_obj_for_used_tree(
    name: str,
    node: Optional[Dict[str, Any]],
    persona: Optional[Dict[str, Any]],
) -> Dict[str, Any]:
    """与 process_pipeline_tree_data._node_obj 一致：合并人设与 edge 上节点字段。"""
    type_val = None
    is_constant = False
    is_local_constant = False
    if persona is not None:
        type_val = persona.get("type")
        if "is_constant" in persona:
            is_constant = bool(persona["is_constant"])
        if "is_local_constant" in persona:
            is_local_constant = bool(persona["is_local_constant"])
    if node is not None:
        t = node.get("type")
        if t is not None and len(t) > 0:
            type_val = t
        if "is_constant" in node:
            is_constant = bool(node["is_constant"])
        if "is_local_constant" in node:
            is_local_constant = bool(node["is_local_constant"])
    return {
        "name": name,
        "type": type_val,
        "is_constant": is_constant,
        "is_local_constant": is_local_constant,
    }


def _dedup_node_objs(nodes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    seen = set()
    out = []
    for n in nodes:
        key = (n["name"], n.get("type"), n["is_constant"], n["is_local_constant"])
        if key not in seen:
            seen.add(key)
            out.append(n)
    return out


def extract_used_tree_nodes_from_edge(
    edge: Dict[str, Any],
    persona_by_name: Dict[str, Dict[str, Any]],
) -> List[Dict[str, Any]]:
    """与 process_pipeline_tree_data.extract_used_tree_nodes_from_edge 一致。"""
    used: List[Dict[str, Any]] = []
    for node in edge.get("input_tree_nodes") or []:
        name = node.get("name")
        if name is None or name == "":
            continue
        persona = persona_by_name.get(name)
        used.append(_node_obj_for_used_tree(name, node, persona))
    for pn in edge.get("input_pattern_nodes") or []:
        for item in pn.get("match_items") or []:
            if item is None or item == "":
                continue
            persona = persona_by_name.get(item)
            used.append(_node_obj_for_used_tree(item, None, persona))
    return _dedup_node_objs(used)


def enrich_visualize_with_used_tree_nodes(
    data: Dict[str, Any],
    persona_by_name: Dict[str, Dict[str, Any]],
) -> Dict[str, Any]:
    """
    为 edge_list 每条 edge 增加 used_tree_nodes，顶层增加 all_used_tree_nodes（与 process_pipeline 一致）。
    """
    edge_list = data.get("edge_list")
    if not edge_list:
        data["all_used_tree_nodes"] = []
        return data

    all_used: List[Dict[str, Any]] = []
    for edge in edge_list:
        used = extract_used_tree_nodes_from_edge(edge, persona_by_name)
        edge["used_tree_nodes"] = used
        all_used.extend(used)

    data["all_used_tree_nodes"] = _dedup_node_objs(all_used)
    return data


def _collect_dimension_names(point_data: dict) -> dict[str, str]:
    """从点的 实质/形式/意图 中收集 名称 -> dimension。"""
    name_to_dim = {}
    if "实质" in point_data and point_data["实质"]:
        for key in ("具体元素", "具象概念", "抽象概念"):
            for item in (point_data["实质"].get(key) or []):
                n = item.get("名称")
                if n:
                    name_to_dim[n] = "实质"
    if "形式" in point_data and point_data["形式"]:
        for key in ("具体元素形式", "具象概念形式", "整体形式"):
            for item in (point_data["形式"].get(key) or []):
                n = item.get("名称")
                if n:
                    name_to_dim[n] = "形式"
    if point_data.get("意图"):
        for item in point_data["意图"]:
            n = item.get("名称")
            if n:
                name_to_dim[n] = "意图"
    return name_to_dim


def parse_topic_points_from_deconstruct(deconstruct_path: Path) -> list[dict[str, Any]]:
    """
    从 input/{account_name}/解构内容/{post_id}.json 解析选题点列表。
    - 新格式（Agent）：灵感点/目的点/关键点 下为「选题点」「选题点元素」（元素名称、元素类型）。
    - 旧格式：「点」「分词结果」中的「词」等。
    输出字段：name, point, dimension, root_source, root_sources_desc。
    """
    if not deconstruct_path.exists():
        raise FileNotFoundError(f"解构内容文件不存在: {deconstruct_path}")
    with open(deconstruct_path, "r", encoding="utf-8") as f:
        data = json.load(f)

    result_agent: list[dict[str, Any]] = []
    for point_type in ("灵感点", "目的点", "关键点"):
        for point in data.get(point_type) or []:
            if not isinstance(point, dict):
                continue
            root_source = (point.get("选题点") or point.get("点") or "").strip()
            root_sources_desc = point.get("选题点描述") or point.get("点描述") or ""
            for el in point.get("选题点元素") or []:
                if not isinstance(el, dict):
                    continue
                name = (el.get("元素名称") or "").strip()
                if not name:
                    continue
                et = el.get("元素类型") or "实质"
                if et not in ("实质", "形式", "意图"):
                    et = "实质"
                result_agent.append(
                    {
                        "name": name,
                        "point": point_type,
                        "dimension": et,
                        "root_source": root_source,
                        "root_sources_desc": root_sources_desc,
                    }
                )
    if result_agent:
        return result_agent

    result = []
    for point_type in ("灵感点", "目的点", "关键点"):
        for point in data.get(point_type) or []:
            root_source = point.get("点", "")
            root_sources_desc = point.get("点描述", "")
            name_to_dim = _collect_dimension_names(point)
            for word_item in point.get("分词结果") or []:
                name = word_item.get("词", "").strip()
                if not name:
                    continue
                dimension = name_to_dim.get(name, "实质")
                result.append({
                    "name": name,
                    "point": point_type,
                    "dimension": dimension,
                    "root_source": root_source,
                    "root_sources_desc": root_sources_desc,
                })
    return result


def _topic_point_key(t: dict) -> tuple:
    return (t["name"], t["point"], t["dimension"])


def load_derivation_logs(log_dir: Path) -> tuple[list[dict], list[dict]]:
    """
    从 output/{account_name}/推导日志/{post_id}/{log_id}/ 读取所有 {轮次}_推导.json 与 {轮次}_评估.json。
    返回 (推导列表按轮次序, 评估列表按轮次序)。
    """
    if not log_dir.is_dir():
        raise FileNotFoundError(f"推导日志目录不存在: {log_dir}")

    derivation_by_round = {}
    eval_by_round = {}
    for p in log_dir.glob("*.json"):
        base = p.stem
        m = re.match(r"^(\d+)_(推导|评估)$", base)
        if not m:
            continue
        round_num = int(m.group(1))
        with open(p, "r", encoding="utf-8") as f:
            content = json.load(f)
        if m.group(2) == "推导":
            derivation_by_round[round_num] = content
        else:
            eval_by_round[round_num] = content

    rounds = sorted(set(derivation_by_round) | set(eval_by_round))
    derivations = [derivation_by_round[r] for r in rounds if r in derivation_by_round]
    evals = [eval_by_round[r] for r in rounds if r in eval_by_round]
    return derivations, evals


def build_derivation_result(
    topic_points: list[dict],
    derivations: list[dict],
    evals: list[dict],
) -> list[dict]:
    """
    生成整体推导结果：每轮 轮次、推导成功的选题点、未推导成功的选题点、本次新推导成功的选题点。
    选题点用 topic_points 中的完整信息；按 name 判定是否被推导（评估中的 match_post_point）。
    若之前推导成功的选题点 is_fully_derived=false，本轮变为 is_fully_derived=true，则算本次新推导成功的选题点，
    且 matched_score、is_fully_derived 在本轮后更新为该轮评估值。
    推导成功的选题点：使用当前已更新的 best (matched_score, is_fully_derived)。
    本次新推导成功的选题点：用当轮评估的 matched_score、is_fully_derived。
    未推导成功的选题点：不包含 matched_score、is_fully_derived。
    """
    all_keys = {_topic_point_key(t) for t in topic_points}
    topic_by_key = {_topic_point_key(t): t for t in topic_points}

    # 分轮次收集 (round_num, name) -> (matched_score, is_fully_derived)，同一轮同名保留 matched_score 最高的
    score_by_round_name: dict[tuple[int, str], tuple[float, bool]] = {}
    for round_idx, eval_data in enumerate(evals):
        round_num = eval_data.get("round", round_idx + 1)
        for er in eval_data.get("eval_results") or []:
            if not (er.get("is_matched") is True or er.get("match_result") == "匹配"):
                continue
            mp = (er.get("matched_post_point") or er.get("matched_post_topic") or er.get("match_post_point") or "").strip()
            if not mp:
                continue
            score = er.get("matched_score")
            if score is None:
                score = 1.0
            else:
                try:
                    score = float(score)
                except (TypeError, ValueError):
                    score = 1.0
            is_fully = er.get("is_fully_derived", True)
            key = (round_num, mp)
            if key not in score_by_round_name or score > score_by_round_name[key][0]:
                score_by_round_name[key] = (score, bool(is_fully))

    result = []
    derived_names_so_far: set[str] = set()
    fully_derived_names_so_far: set[str] = set()  # 已出现过 is_fully_derived=true 的选题点
    # name -> (matched_score, is_fully_derived)，一旦 is_fully_derived=True，后续轮次不再更新 matched_score
    best_score_by_name: dict[str, tuple[float, bool]] = {}

    for i, (derivation, eval_data) in enumerate(zip(derivations, evals)):
        round_num = derivation.get("round", i + 1)
        eval_results = eval_data.get("eval_results") or []
        matched_post_points = set()
        for er in eval_results:
            if not (er.get("is_matched") is True or er.get("match_result") == "匹配"):
                continue
            mp = er.get("matched_post_point") or er.get("matched_post_topic") or er.get("match_post_point") or ""
            if mp and str(mp).strip():
                matched_post_points.add(str(mp).strip())

        # 本轮每个匹配名的 (score, is_fully)
        this_round_scores: dict[str, tuple[float, bool]] = {}
        for name in matched_post_points:
            val = score_by_round_name.get((round_num, name))
            if val is not None:
                this_round_scores[name] = val

        # 本次新推导成功：首次匹配 或 之前 is_fully=false 且本轮 is_fully=true
        new_derived_names = set()
        for name in matched_post_points:
            score, is_fully = this_round_scores.get(name, (None, False))
            if name not in derived_names_so_far:
                new_derived_names.add(name)
            elif name not in fully_derived_names_so_far and is_fully:
                new_derived_names.add(name)

        # 更新推导集合与 best：
        # - 首次出现时写入
        # - 若尚未 fully 且本轮 fully，则更新为 fully，并锁定，不再被后续轮次覆盖
        # - 若尚未 fully 且本轮仍为部分推导，可用更高分数更新
        derived_names_so_far |= matched_post_points
        for name in matched_post_points:
            val = this_round_scores.get(name)
            if val is None:
                continue
            score, is_fully = val
            if name not in best_score_by_name:
                best_score_by_name[name] = (score, is_fully)
            else:
                prev_score, prev_fully = best_score_by_name[name]
                # 已经 fully 的节点，后续轮次不再更新 matched_score
                if prev_fully:
                    pass
                else:
                    if is_fully:
                        best_score_by_name[name] = (score, True)
                    else:
                        # 都是部分推导时，可以用更高分覆盖
                        if score > prev_score:
                            best_score_by_name[name] = (score, False)
            if is_fully:
                fully_derived_names_so_far.add(name)

        derived_keys = {k for k in all_keys if topic_by_key[k]["name"] in derived_names_so_far}
        new_derived_keys = {k for k in all_keys if topic_by_key[k]["name"] in new_derived_names}
        not_derived_keys = all_keys - derived_keys

        sort_derived = sorted(derived_keys, key=lambda k: (topic_by_key[k]["name"], k[1], k[2]))
        sort_new = sorted(new_derived_keys, key=lambda k: (topic_by_key[k]["name"], k[1], k[2]))
        sort_not = sorted(not_derived_keys, key=lambda k: (topic_by_key[k]["name"], k[1], k[2]))

        def add_score_fields(keys: set, sort_keys: list, round_for_score: int | None) -> list[dict]:
            """round_for_score: 用该轮评估的分数；若为 None 则不添加 score 字段。"""
            out = []
            for k in sort_keys:
                if k not in keys:
                    continue
                obj = dict(topic_by_key[k])
                if round_for_score is not None:
                    name = obj.get("name", "")
                    val = score_by_round_name.get((round_for_score, name))
                    if val is not None:
                        obj["matched_score"] = val[0]
                        obj["is_fully_derived"] = val[1]
                    else:
                        obj["matched_score"] = None
                        obj["is_fully_derived"] = False
                out.append(obj)
            return out

        # 推导成功的选题点：用当前已更新的 best (matched_score, is_fully_derived)
        derived_list = []
        for k in sort_derived:
            if k not in derived_keys:
                continue
            obj = dict(topic_by_key[k])
            name = obj.get("name", "")
            val = best_score_by_name.get(name)
            if val is not None:
                obj["matched_score"] = val[0]
                obj["is_fully_derived"] = val[1]
            else:
                obj["matched_score"] = None
                obj["is_fully_derived"] = False
            derived_list.append(obj)

        new_list = add_score_fields(new_derived_keys, sort_new, round_for_score=round_num)
        not_derived_list = [dict(topic_by_key[k]) for k in sort_not]  # 不带 matched_score、is_fully_derived

        result.append({
            "轮次": round_num,
            "推导成功的选题点": derived_list,
            "未推导成功的选题点": not_derived_list,
            "本次新推导成功的选题点": new_list,
        })
    return result


def _tree_node_display_name(raw: str) -> str:
    """人设节点可能是 a.b.c 路径形式，实际需要的是最后一段节点名 c。"""
    s = (raw or "").strip()
    if "." in s:
        return s.rsplit(".", 1)[-1].strip() or s
    return s


def _to_tree_node(name: str, extra: dict | None = None) -> dict:
    d = {"name": name}
    if extra:
        d.update(extra)
    return d


def _to_pattern_node(pattern_name: str) -> dict:
    """将 pattern 字符串转为 input_pattern_nodes 的一项（简化版）。"""
    items = [x.strip() for x in pattern_name.replace("+", " ").split() if x.strip()]
    return {
        "items": [{"name": x, "point": "关键点", "dimension": "形式", "type": "标签"} for x in items],
        "match_items": items,
    }


def build_visualize_edges(
    derivations: list[dict],
    evals: list[dict],
    topic_points: list[dict],
) -> tuple[list[dict], list[dict]]:
    """
    生成 node_list（所有评估通过的帖子选题点）和 edge_list（只保留评估通过的推导路径）。
    - node_list：同一轮内节点不重复，重复时保留 matched_score 更高的；节点带 matched_score、is_fully_derived。
    - edge_list：边带 level（与 output 节点 level 一致）；同一轮内 output 节点不重复；若前面轮次该节点匹配分更高则本轮不保留该节点。
    评估数据支持 path_id（对应推导 derivation_results[].id）、derivation_output_point（与推导 output 中字符串对齐）、matched_score、is_fully_derived；不按 item_id 对齐。
    """
    derivations = sorted(derivations, key=lambda d: d.get("round", 0))
    evals = sorted(evals, key=lambda e: e.get("round", 0))

    topic_by_name = {t["name"]: t for t in topic_points}

    # 评估匹配：(round_num, path_id, derivation_output_point) -> (matched_post_point, matched_reason, matched_score, is_fully_derived)
    match_by_path_out: dict[tuple[int, int, str], tuple[str, str, float, bool]] = {}
    match_by_round_output: dict[tuple[int, str], tuple[str, str, float, bool]] = {}  # 兼容无 path_id 的旧数据
    for round_idx, eval_data in enumerate(evals):
        round_num = eval_data.get("round", round_idx + 1)
        for er in eval_data.get("eval_results") or []:
            if not (er.get("is_matched") is True or er.get("match_result") == "匹配"):
                continue
            mp = (er.get("matched_post_point") or er.get("matched_post_topic") or er.get("match_post_point") or "").strip()
            if not mp:
                continue
            out_point = (er.get("derivation_output_point") or "").strip()
            reason = (er.get("matched_reason") or er.get("match_reason") or "").strip()
            score = er.get("matched_score")
            if score is None:
                score = 1.0
            else:
                try:
                    score = float(score)
                except (TypeError, ValueError):
                    score = 1.0
            is_fully = er.get("is_fully_derived", True)
            val = (mp, reason, score, bool(is_fully))
            path_id = er.get("path_id")
            if path_id is not None and out_point:
                try:
                    match_by_path_out[(round_num, int(path_id), out_point)] = val
                except (TypeError, ValueError):
                    pass
            if out_point:
                k = (round_num, out_point)
                if k not in match_by_round_output:
                    match_by_round_output[k] = val

    def get_match(round_num: int, path_id: int | None, out_item: str) -> tuple[str, str, float, bool] | None:
        out_item = (out_item or "").strip()
        if not out_item:
            return None
        if path_id is not None:
            v = match_by_path_out.get((round_num, path_id, out_item))
            if v is not None:
                return v
        return match_by_round_output.get((round_num, out_item))

    # 第一遍：按 (round_num, mp) 聚合节点最佳信息（不考虑边是否最终保留）
    # (round_num, mp) -> (score, is_fully_derived, derivation_output_point, method)
    best_node_info_by_round_mp: dict[tuple[int, str], tuple[float, bool, str, str]] = {}
    for round_idx, derivation in enumerate(derivations):
        round_num = derivation.get("round", round_idx + 1)
        for dr in derivation.get("derivation_results") or []:
            output_list = dr.get("output") or []
            path_id = dr.get("id")
            for out_item in output_list:
                v = get_match(round_num, path_id, out_item)
                if not v:
                    continue
                mp, _reason, score, is_fully = v
                key = (round_num, mp)
                prev = best_node_info_by_round_mp.get(key)
                if prev is None or score > prev[0]:
                    best_node_info_by_round_mp[key] = (score, bool(is_fully), out_item, dr.get("method", ""))

    edge_list = []
    round_output_seen: set[tuple[int, str]] = set()  # (round_num, node_name) 本轮已作为某边的 output
    prev_best_by_node: dict[str, tuple[float, bool]] = {}  # node_name -> (score, is_fully) of last included round

    for round_idx, derivation in enumerate(derivations):
        round_num = derivation.get("round", round_idx + 1)
        for dr in derivation.get("derivation_results") or []:
            output_list = dr.get("output") or []
            path_id = dr.get("id")
            matched: list[tuple[str, str, float, bool, str]] = []  # (mp, reason, score, is_fully, derivation_out)
            for out_item in output_list:
                v = get_match(round_num, path_id, out_item)
                if not v:
                    continue
                mp, reason, score, is_fully = v
                matched.append((mp, reason, score, is_fully, out_item))

            if not matched:
                continue

            # 同一轮内 output 节点不重复；若前面轮次该节点已完全推导，或分数未提升且未从 false 变 true，则本轮跳过；
            # 并且只保留与 node_list 中该轮该节点的最高分记录一致的边
            output_names_this_edge = []
            for mp, reason, score, is_fully, out_item in matched:
                if (round_num, mp) in round_output_seen:
                    continue
                prev = prev_best_by_node.get(mp)
                if prev is not None:
                    prev_score, prev_fully = prev
                    if prev_fully:
                        continue
                    if not is_fully and score <= prev_score:
                        continue
                best_info = best_node_info_by_round_mp.get((round_num, mp))
                if not best_info or score < best_info[0]:
                    continue
                output_names_this_edge.append((mp, reason, score, is_fully, out_item))

            if not output_names_this_edge:
                continue

            for mp, _r, score, is_fully, _o in output_names_this_edge:
                round_output_seen.add((round_num, mp))
                prev = prev_best_by_node.get(mp)
                if prev is None or (not prev[1] and (is_fully or score > prev[0])):
                    prev_best_by_node[mp] = (score, is_fully)

            input_data = dr.get("input") or {}
            derived_nodes = input_data.get("derived_nodes") or []
            tree_nodes = input_data.get("tree_nodes") or []
            patterns = input_data.get("patterns") or []

            input_post_nodes = [{"name": x} for x in derived_nodes]
            input_tree_nodes = [_to_tree_node(_tree_node_display_name(x)) for x in tree_nodes]
            if patterns and isinstance(patterns[0], str):
                input_pattern_nodes = [_to_pattern_node(p) for p in patterns]
            elif patterns and isinstance(patterns[0], dict):
                input_pattern_nodes = patterns
            else:
                input_pattern_nodes = []

            output_nodes = []
            reasons_list = []
            derivation_points_list = []
            for mp, reason, score, is_fully, out_item in output_names_this_edge:
                output_nodes.append({"name": mp, "matched_score": score, "is_fully_derived": is_fully})
                reasons_list.append(reason)
                derivation_points_list.append(out_item)

            detail = {
                "reason": dr.get("reason", ""),
                "评估结果": "匹配成功",
            }
            if any(reasons_list):
                detail["匹配理由"] = reasons_list
            detail["待比对的推导选题点"] = derivation_points_list
            if dr.get("tools"):
                detail["tools"] = dr["tools"]
            edge_list.append({
                "name": dr.get("method", "") or f"推导-{round_num}",
                "level": round_num,
                "input_post_nodes": input_post_nodes,
                "input_tree_nodes": input_tree_nodes,
                "input_pattern_nodes": input_pattern_nodes,
                "output_nodes": output_nodes,
                "detail": detail,
            })

    # 根据按 (round, mp) 聚合后的最佳信息生成 node_list
    # 规则：节点首次出现保留；is_fully_derived 从 false 变 true 时保留；
    # is_fully_derived=false 且分数高于之前已保留轮次时保留；其余情况跳过
    prev_node_best: dict[str, tuple[float, bool]] = {}  # mp -> (score, is_fully) of last included round
    node_list: list[dict] = []
    for (round_num, mp), (score, is_fully, out_item, method) in sorted(
        best_node_info_by_round_mp.items(), key=lambda x: (x[0][0], x[0][1])
    ):
        prev = prev_node_best.get(mp)
        if prev is None:
            should_include = True
        else:
            prev_score, prev_fully = prev
            if prev_fully:
                should_include = False
            elif is_fully:
                should_include = True
            elif score > prev_score:
                should_include = True
            else:
                should_include = False
        if not should_include:
            continue
        prev_node_best[mp] = (score, is_fully)
        base = dict(topic_by_name.get(mp, {"name": mp, "point": "", "dimension": "", "root_source": "", "root_sources_desc": ""}))
        base["level"] = round_num
        base.setdefault("original_word", base.get("name", mp))
        base["derivation_type"] = method
        base["matched_score"] = score
        base["is_fully_derived"] = is_fully
        base["derivation_output_point"] = out_item
        node_list.append(base)

    node_list.sort(key=lambda n: (n.get("level", 0), str(n.get("name", ""))))
    return node_list, edge_list


def _find_project_root() -> Path:
    """从脚本所在目录向上查找包含 .git 的项目根目录。"""
    p = Path(__file__).resolve().parent
    while p != p.parent:
        if (p / ".git").is_dir():
            return p
        p = p.parent
    return Path(__file__).resolve().parent


def generate_visualize_data(account_name: str, post_id: str, log_id: str, base_dir: Path | None = None) -> None:
    """
    主流程：读取解构内容与推导日志，生成整体推导结果与整体推导路径可视化两个 JSON。
    base_dir 默认为脚本所在目录；若其下 output/.../推导日志 不存在，则尝试项目根目录下的 output/...（兼容从项目根运行）。
    """
    if base_dir is None:
        base_dir = Path(__file__).resolve().parent
    input_dir = base_dir / "input" / account_name / "原始数据" / "解构内容"
    log_dir = base_dir / "output" / account_name / "推导日志" / post_id / log_id
    result_dir = base_dir / "output" / account_name / "整体推导结果"
    visualize_dir = base_dir / "output" / account_name / "整体推导路径可视化"
    # 兼容：若推导日志不在 base_dir 下，尝试项目根目录下的 output/
    if not log_dir.is_dir():
        project_root = _find_project_root()
        if project_root != base_dir:
            alt_log = project_root / "output" / account_name / "推导日志" / post_id / log_id
            if alt_log.is_dir():
                log_dir = alt_log
                result_dir = project_root / "output" / account_name / "整体推导结果"
                visualize_dir = project_root / "output" / account_name / "整体推导路径可视化"

    deconstruct_path = input_dir / f"{post_id}.json"
    topic_points = parse_topic_points_from_deconstruct(deconstruct_path)

    derivations, evals = load_derivation_logs(log_dir)
    if not derivations or not evals:
        raise ValueError(f"推导或评估数据为空: {log_dir}")

    # 2.1 整体推导结果
    derivation_result = build_derivation_result(topic_points, derivations, evals)
    result_dir.mkdir(parents=True, exist_ok=True)
    result_path = result_dir / f"{post_id}.json"
    with open(result_path, "w", encoding="utf-8") as f:
        json.dump(derivation_result, f, ensure_ascii=False, indent=4)
    print(f"已写入整体推导结果: {result_path}")

    # 2.2 整体推导路径可视化（人设节点补全：used_tree_nodes / all_used_tree_nodes，数据来自处理后数据/tree 人设树）
    node_list, edge_list = build_visualize_edges(derivations, evals, topic_points)
    tree_dir = base_dir / "input" / account_name / "处理后数据" / "tree"
    persona_by_name = build_persona_by_name_from_tree_dir(tree_dir)
    if persona_by_name:
        print(
            f"已加载人设树节点: {len(persona_by_name)} 个（目录: {tree_dir.name}）"
        )
    else:
        print(
            f"警告: 未从人设树目录加载到节点（请确认存在 *_point_tree_how.json）: {tree_dir}"
        )
    visualize_payload: Dict[str, Any] = {"node_list": node_list, "edge_list": edge_list}
    enrich_visualize_with_used_tree_nodes(visualize_payload, persona_by_name)

    visualize_path = visualize_dir / f"{post_id}.json"
    visualize_dir.mkdir(parents=True, exist_ok=True)
    with open(visualize_path, "w", encoding="utf-8") as f:
        json.dump(visualize_payload, f, ensure_ascii=False, indent=4)
    print(f"已写入整体推导路径可视化: {visualize_path}")


def main(account_name, post_id, log_id):
    # parser = argparse.ArgumentParser(description="生成推导可视化数据")
    # parser.add_argument("account_name", help="账号名，如 家有大志")
    # parser.add_argument("post_id", help="帖子 ID")
    # parser.add_argument("log_id", help="推导日志 ID，如 20260303204232")
    # parser.add_argument("--base-dir", type=Path, default=None, help="项目根目录，默认为本脚本所在目录")
    # args = parser.parse_args()
    generate_visualize_data(account_name=account_name, post_id=post_id, log_id=log_id)


if __name__ == "__main__":
    from tools.pattern_dimension_analyze import main as pattern_dimension_analyze_main

    # account_name="阿里多多酱"
    # items = [
    #     {"post_id": "6915dfc400000000070224d9", "log_id": "20260322135142"},
    #     {"post_id":"69002ba70000000007008bcc","log_id":"20260322213934"},
    # ]

    # account_name="摸鱼阿希"
    # items = [
    #     {"post_id": "68ae91ce000000001d016b8b", "log_id": "20260322202416"},
    #     {"post_id":"689c63ac000000001d015119","log_id":"20260322203119"},
    # ]

    # account_name = "每天心理学"
    # items = [
    #     {"post_id": "6949df27000000001d03e0e9", "log_id": "20260322205512"},
    #     {"post_id": "6951c718000000001e0105b7", "log_id": "20260322211126"},
    # ]

    account_name = "空间点阵设计研究室"
    items = [
        {"post_id": "687ee6fc000000001c032bb1", "log_id": "20260322211748"},
        {"post_id": "68843a4d000000001c037591", "log_id": "20260322213024"},
    ]

    for item in items:
        post_id = item["post_id"]
        log_id = item["log_id"]
        main(account_name, post_id, log_id)
        pattern_dimension_analyze_main(account_name, post_id, log_id)