修复计算内核

zsim/data/APLData/柚叶-雅-薇薇安.toml

@@ -3,7 +3,7 @@ title = "柚叶-雅-薇薇安"
 comment = "开发组为 柚叶、雅、薇薇安 提供的默认APL"
 author = "虎皮"
 create_time = "2025-07-15T14:06:38.457+08:00"
-latest_change_time = "2025-07-26T09:39:24.292+08:00"
+latest_change_time = "2025-07-27T03:23:03.891+08:00"
 
 [characters]
 required = [ "柚叶", "薇薇安", "雅",]

zsim/data/buff_effect.csv

@@ -466,7 +466,7 @@ Buff-角色-薇薇安-核心被动触发器,,,,,,,,
 Buff-角色-薇薇安-预言触发器,,,,,,,,
 Buff-角色-薇薇安-额外能力-协同攻击触发器,,,,,,,,
 Buff-角色-薇薇安-额外能力-全队侵蚀伤害增加,侵蚀额外伤害增幅,0.12,,,,,,
-Buff-角色-薇薇安-额外能力-侵蚀紊乱伤害提升,侵蚀紊乱伤害提升,0.12,,,,,,
+Buff-角色-薇薇安-额外能力-侵蚀紊乱伤害提升,紊乱额外伤害增幅,0.12,,,,,,
 Buff-角色-薇薇安-1画-全属性异常和紊乱伤害提升,全属性异常额外伤害增幅,0.16,紊乱额外伤害增幅,0.16,,,,
 Buff-角色-薇薇安-2画-以太积蓄效率提升,以太积蓄效率增加,0.25,,,,,,
 Buff-角色-薇薇安-2画-异放全属性抗性穿透,全属性抗性穿透,0.15,,,,,,

zsim/data/character_config_example.toml

@@ -169,7 +169,7 @@ equip_set2_a = "激素朋克"
 name = "薇薇安"
 weapon = "飞鸟星梦"
 weapon_level = 1
-cinema = 6
+cinema = 0
 crit_balancing = true
 scATK_percent = 5
 scATK = 0

zsim/data/skill.csv

@@ -863,4 +863,4 @@
 1411,柚叶,普攻,1411_CoAttack_C,协同攻击（狸猫）,普通攻击：狸之助,,狸猫阿釜自主攻击,1.1,0.1,2.2,2.4,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,85,8,FALSE,TRUE,0,0,,,,0,0,"[15, 20, 24, 33, 41, 60, 63, 68]",,FALSE,1,FALSE,
 1411,柚叶,支援突击,1411_Assault_Aid_A,突击支援（开伞格挡后）,支援突击：夹心硬糖射击,,,4.636,0.422,9.278,10.122,10.966,4.087,0.186,6.133,6.505,6.877,0,0,0,0,0,0,0,129.635,221.64,0,0,39642,6,9,0,1,0,137,6,TRUE,TRUE,0,0,,,,0,0,"[28, 35, 42, 51, 98, 113]",,TRUE,1,FALSE,
 1411,柚叶,附加伤害,1411_Cinema_6,6画炮弹,强力炮弹,,,0.3,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,FALSE,FALSE,0,0,"{'additional_damage':1,'sugar_points':-1}",,,0,0,,,FALSE,1,FALSE,
-1411,柚叶,支援突击,1411_Assault_Aid_B,突击支援（6画专属）,支援突击：夹心硬糖射击,,6画专属蓄力版支援突击,4.636,0.422,9.278,10.122,10.966,4.087,0.186,6.133,6.505,6.877,0,0,0,0,0,0,0,129.635,221.64,0,0,39642,6,9,0,1,0,185,6,TRUE,TRUE,0,0,,,,0,0,"[28, 35, 42, 51, 146, 161]",,TRUE,1,FALSE,
+1411,柚叶,支援突击,1411_Assault_Aid_B,突击支援（6画专属）,支援突击：夹心硬糖射击,,6画专属蓄力版支援突击,4.636,0.422,9.278,10.122,10.966,4.087,0.186,6.133,6.505,6.877,0,0,0,0,0,0,0,129.635,221.64,0,0,39642,6,9,0,1,0,137,6,TRUE,TRUE,0,0,,,,0,0,"[28, 35, 42, 51, 122, 136]",,TRUE,1,FALSE,

zsim/data/激活判断.csv

@@ -466,7 +466,7 @@ Buff-角色-薇薇安-核心被动触发器,FALSE,FALSE,FALSE,FALSE,薇薇安,FA
 Buff-角色-薇薇安-预言触发器,FALSE,FALSE,FALSE,FALSE,薇薇安,FALSE,添加核心被动Dot的触发器,FALSE,0,1,1,FALSE,FALSE,TRUE,FALSE,TRUE,0,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,TRUE,0,1000,FALSE,FALSE,TRUE,,,
 Buff-角色-薇薇安-额外能力-协同攻击触发器,FALSE,FALSE,TRUE,FALSE,薇薇安,FALSE,队友触发属性异常时，协同释放一次落雨生花,FALSE,0,1,1,FALSE,FALSE,TRUE,FALSE,TRUE,0,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,TRUE,0,1000,TRUE,FALSE,TRUE,,,
 Buff-角色-薇薇安-额外能力-全队侵蚀伤害增加,FALSE,TRUE,TRUE,FALSE,薇薇安,FALSE,全队造成的侵蚀伤害提高12%,FALSE,0,1,1,FALSE,FALSE,TRUE,TRUE,FALSE,0,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,0,1,FALSE,FALSE,FALSE,,,
-Buff-角色-薇薇安-额外能力-侵蚀紊乱伤害提升,FALSE,TRUE,TRUE,FALSE,薇薇安,FALSE,侵蚀状态被结算的紊乱伤害提升12%,FALSE,0,1,1,FALSE,FALSE,TRUE,TRUE,FALSE,0,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,0,1,FALSE,FALSE,FALSE,,,
+Buff-角色-薇薇安-额外能力-侵蚀紊乱伤害提升,FALSE,TRUE,TRUE,FALSE,薇薇安,FALSE,侵蚀状态被结算的紊乱伤害提升12%,FALSE,0,1,1,FALSE,FALSE,TRUE,TRUE,FALSE,0,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,0,1,FALSE,FALSE,FALSE,"{""specified_disorder_element_type"":[4,6]}",,
 Buff-角色-薇薇安-1画-全属性异常和紊乱伤害提升,FALSE,TRUE,FALSE,TRUE,薇薇安,FALSE,处于薇薇安预言下的目标受到的所有属性异常伤害和紊乱伤害提高16%,FALSE,999999,1,1,FALSE,FALSE,TRUE,FALSE,TRUE,0,TRUE,FALSE,TRUE,TRUE,TRUE,TRUE,TRUE,1,1,FALSE,FALSE,TRUE,,,
 Buff-角色-薇薇安-2画-以太积蓄效率提升,FALSE,FALSE,FALSE,TRUE,薇薇安,FALSE,以太异常积蓄效率提升25%,FALSE,0,1,1,FALSE,FALSE,TRUE,TRUE,FALSE,0,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,2,1000,FALSE,FALSE,FALSE,,,
 Buff-角色-薇薇安-2画-异放全属性抗性穿透,FALSE,TRUE,FALSE,TRUE,薇薇安,FALSE,专属于异放的15%全属性伤害抗性穿透,FALSE,0,1,1,FALSE,FALSE,TRUE,TRUE,FALSE,0,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,2,1,FALSE,FALSE,TRUE,"{""only_anomaly"":[""Abloom""]}",,

zsim/define.py

@@ -104,7 +104,14 @@ def update_json_config(template: dict, user: dict) -> bool:
 PARRY_BASE_PARAMETERS: dict[str, int | float] = {
     "ChainParryActionTimeCost": 10,  # 连续招架动作的时间消耗
 }
-CHAR_PARRY_STRATEGY_MAP: dict = {1411: "1411_Assault_Aid_A"}
+
+# 招架策略，有些角色的拥有不同的突击支援（比如柚叶），所以在这里用字典进行映射。
+# 该字典的key为CID，value为招架动作的skill_tag
+# 注意，不同的招架策略有时候存在着影画或是其他的限制条件，
+# 所以若是在不满足这些条件的情况下强行使用这些招架策略，那么character中的审查函数会报错而中断程序运行。
+CHAR_PARRY_STRATEGY_MAP: dict = {
+    1411: "1411_Assault_Aid_A"
+}
 
 # debug参数，用于检查APL在窗口期间的想法
 APL_THOUGHT_CHECK: bool = _config["apl_mode"].get("apl_thought_check", False)

zsim/script/draw_anomaly_timeline.py

@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+"""
+绘制异常轴图的脚本。
+该脚本读取本地results目录下的damage.csv文件，并根据其中的信息绘制一个透明背景的异常轴图。
+
+
+启动命令：python zsim/script/draw_anomaly_timeline.py results/359
+
+"""
+
+import os
+import sys
+import argparse
+try:
+    import pandas as pd
+    import plotly.express as px
+    import plotly.graph_objects as go
+    PLOTLY_AVAILABLE = True
+except ImportError:
+    PLOTLY_AVAILABLE = False
+    print("错误: 缺少必要的依赖包。请安装 pandas 和 plotly:")
+    print("  pip install pandas plotly")
+    sys.exit(1)
+
+
+def find_consecutive_true_ranges(df, column):
+    """查找DataFrame列中连续为True的范围。
+
+    Args:
+        df (pd.DataFrame): 输入的DataFrame，需要包含 'tick' 列。
+        column (str): 要查找的布尔列名。
+
+    Returns:
+        list[tuple[int, int]]: 一个包含 (开始tick, 结束tick) 元组的列表。
+    """
+    ranges = []
+    start = None
+
+    # 获取tick列和指定列的值
+    ticks = df["tick"].tolist()
+    values = df[column].tolist()
+
+    for i, (tick, value) in enumerate(zip(ticks, values)):
+        if value:
+            if start is None:
+                start = tick
+        else:
+            if start is not None:
+                # 结束tick应该是上一个为True的tick
+                prev_tick = ticks[i - 1] if i > 0 else start
+                ranges.append((start, prev_tick))
+                start = None
+    # 处理最后一个区间（如果存在）
+    if start is not None:
+        ranges.append((start, ticks[-1]))
+    return ranges
+
+
+def prepare_timeline_data(df):
+    """准备用于绘制异常状态时间线的数据。
+
+    Args:
+        df (pd.DataFrame): 原始伤害数据。
+
+    Returns:
+        tuple: (用于绘制Gantt图的DataFrame, 每种异常状态的平均持续时间字典)
+    """
+    required_columns = [
+        "冻结",
+        "霜寒",
+        "畏缩",
+        "感电",
+        "灼烧",
+        "侵蚀",
+        "烈霜霜寒",
+        "tick",
+    ]
+    missing_cols = [col for col in required_columns if col not in df.columns]
+    if missing_cols:
+        raise ValueError(f"输入数据缺少必要的列: {missing_cols}")
+
+    columns_to_check = ["冻结", "霜寒", "畏缩", "感电", "灼烧", "侵蚀", "烈霜霜寒"]
+    gantt_data = []
+    duration_stats = {}
+
+    for col in columns_to_check:
+        if col in df.columns:
+            ranges = find_consecutive_true_ranges(df, col)
+            durations = [end - start + 1 for start, end in ranges]  # 持续时间包含首尾
+            duration_stats[col] = durations
+
+            for start, end in ranges:
+                gantt_data.append({"Task": col, "Start": start, "Finish": end})
+
+    if not gantt_data:
+        return pd.DataFrame(), {}
+
+    gantt_df = pd.DataFrame(gantt_data)
+    gantt_df["Duration"] = gantt_df["Finish"] - gantt_df["Start"] + 1  # 持续时间包含首尾
+
+    # 计算每种异常状态的平均持续时间
+    avg_durations = {}
+    for col, durations in duration_stats.items():
+        if durations:
+            avg_durations[col] = sum(durations) / len(durations)
+        else:
+            avg_durations[col] = 0
+
+    return gantt_df, avg_durations
+
+
+def draw_anomaly_timeline(gantt_df, output_path=None):
+    """绘制异常状态时间线（Gantt图）。
+
+    Args:
+        gantt_df (pd.DataFrame): 用于绘制Gantt图的数据。
+        output_path (str, optional): 输出图片文件路径（例如 output.png）。
+    """
+    if gantt_df is not None and len(gantt_df) > 0:
+        fig = px.bar(
+            gantt_df,
+            x="Duration",
+            y="Task",
+            base="Start",
+            orientation="h",
+            labels={
+                "Start": "开始时间(帧)",
+                "Duration": "持续时间(帧)",
+                "Task": "状态类型",
+            },
+            height=350,
+        )
+
+        # 设置透明背景
+        fig.update_layout(
+            plot_bgcolor='rgba(0,0,0,0)',
+            paper_bgcolor='rgba(0,0,0,0)',
+        )
+
+        # 设置网格线颜色
+        fig.update_xaxes(showgrid=True, gridwidth=3, gridcolor='rgba(128,128,128,0.2)')
+        fig.update_yaxes(showgrid=True, gridwidth=3, gridcolor='rgba(128,128,128,0.2)')
+
+        if output_path:
+            # 保存为PNG图片
+            try:
+                fig.write_image(output_path, width=1200, height=300)
+                print(f"异常轴图已保存至: {output_path}")
+            except ValueError as e:
+                if "kaleido" in str(e).lower():
+                    print("错误: 缺少kaleido包，无法保存图片。请安装kaleido:")
+                    print("  pip install kaleido")
+                    print("或者只在浏览器中查看图表，不使用 -o 参数")
+                    sys.exit(1)
+                else:
+                    raise e
+        else:
+            # 在浏览器中显示
+            fig.show()
+    else:
+        print("没有找到任何连续的状态数据")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="绘制异常轴图")
+    parser.add_argument("result_dir", help="战斗日志所在的结果目录，例如 results/123")
+    parser.add_argument("-o", "--output", help="输出图片文件路径（例如 output.png）")
+
+    args = parser.parse_args()
+
+    # 如果指定了输出路径，检查kaleido是否可用
+    if args.output:
+        try:
+            import plotly.io as pio
+            # 尝试导入kaleido
+            pio.kaleido.scope
+        except ImportError:
+            print("警告: 如果要保存图片，请安装kaleido:")
+            print("  pip install kaleido")
+            print("否则请只在浏览器中查看图表，不使用 -o 参数")
+
+    # 构建damage.csv文件路径
+    damage_csv_path = os.path.join(args.result_dir, "damage.csv")
+
+    # 检查文件是否存在
+    if not os.path.exists(damage_csv_path):
+        print(f"错误: 文件 {damage_csv_path} 不存在")
+        sys.exit(1)
+
+    # 读取damage.csv文件
+    try:
+        df = pd.read_csv(damage_csv_path)
+        print(f"成功读取文件: {damage_csv_path}")
+    except Exception as e:
+        print(f"读取文件时出错: {e}")
+        sys.exit(1)
+
+    # 准备数据
+    try:
+        gantt_df, avg_durations = prepare_timeline_data(df)
+        print("数据准备完成")
+    except Exception as e:
+        print(f"准备数据时出错: {e}")
+        sys.exit(1)
+
+    # 输出每种异常状态的平均持续时间
+    print("\n各异常状态的平均持续时间:")
+    print("-" * 30)
+    for anomaly, avg_duration in avg_durations.items():
+        if avg_duration == 0:
+            continue
+        print(f"{anomaly}: {avg_duration/60:.2f} 秒")
+    print("-" * 30)
+
+    # 绘制图表
+    try:
+        draw_anomaly_timeline(gantt_df, args.output)
+        print("异常轴图绘制完成")
+    except Exception as e:
+        print(f"绘制图表时出错: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

zsim/sim_progress/Buff/BuffXLogic/YuzuhaCinema6SheelTrigger.py

@@ -50,7 +50,7 @@ def special_judge_logic(self, **kwargs):
             return False
         tick = self.buff_instance.sim_instance.tick
         lasting_tick = tick - skill_node.preload_tick
-        if 0 <= lasting_tick < 51:
+        if 0 <= lasting_tick < 24:
             return False
         else:
             self.record.charging_start = True