feat: 增加更多支援卡/允许ura剧本配置支援卡启发权重 (#202)

* feat: 支援卡选择页面框架

* feat: 重构支援卡保存结构，添加更多支援卡

* feat: 选择支援卡时渲染支援卡图片

* fix: 编译和小型调整前端

* feat: 允许ura剧本配置支援卡启发权重

Author: ZJChe

module/umamusume/scenario/configs.py

@@ -1,3 +1,29 @@
+class UraConfig:
+    skill_event_weight: list[int]
+    reset_skill_event_weight_list: list[str]
+
+    def __init__(self, config: dict):
+        if "skillEventWeight" not in config or "resetSkillEventWeightList" not in config:
+            raise ValueError("错误的配置: 必须配置 'skillEventWeight' 和 'resetSkillEventWeightList'")
+        self.skill_event_weight = config["skillEventWeight"]
+        self.reset_skill_event_weight_list = config["resetSkillEventWeightList"]
+    
+    def removeSkillFromList(self, skill: str):
+        if skill in self.reset_skill_event_weight_list:
+            self.reset_skill_event_weight_list.remove(skill)
+            # 如果技能列表空了, 重置权重
+            # 如果一开始列表就是空的, 这个分支就不会触发, 也不会重置权重
+            if len(self.reset_skill_event_weight_list) == 0:
+                self.skill_event_weight = [0, 0, 0]
+    
+    def getSkillEventWeight(self, date: int) -> int:
+        if date <= 24:
+            return self.skill_event_weight[0]
+        elif date <= 48:
+            return self.skill_event_weight[1]
+        else:
+            return self.skill_event_weight[2]
+
 class AoharuConfig:
 
     preliminary_round_selections: list[int]
@@ -17,8 +43,9 @@ def get_opponent(self, round_index: int) -> int:
 
 class ScenarioConfig:
     """ 所有场景的配置 """
-
+    ura_config: UraConfig = None
     aoharu_config: AoharuConfig = None
 
-    def __init__(self, aoharu_config: AoharuConfig = None):
+    def __init__(self, ura_config: UraConfig = None, aoharu_config: AoharuConfig = None):
+        self.ura_config = ura_config
         self.aoharu_config = aoharu_config

module/umamusume/script/cultivate_task/ai.py

@@ -14,6 +14,7 @@ def get_operation(ctx: UmamusumeContext) -> TurnOperation | None:
     attribute_result = get_training_basic_attribute_score(ctx, ctx.cultivate_detail.turn_info,
                                                           ctx.cultivate_detail.expect_attribute)
     support_card_result = get_training_support_card_score(ctx)
+    support_card_event_result = get_support_card_event_score(ctx)
     training_level_result = get_training_level_score(ctx)
 
     attribute_result_max = np.max(attribute_result)
@@ -79,7 +80,8 @@ def get_operation(ctx: UmamusumeContext) -> TurnOperation | None:
     training_score = []
     for i in range(5):
         training_score.append(normalized_attribute_result[i] * attr_weight + normalized_support_card_result[i] *
-                              support_card_weight + normalized_training_level_result[i] * training_level_weight)
+                              support_card_weight + normalized_training_level_result[i] * training_level_weight + 
+                              support_card_event_result[i])
     # 将权重为-1的训练得分置为0    
     extra_weight = [0, 0, 0, 0, 0]
     date = ctx.cultivate_detail.turn_info.date
@@ -151,7 +153,7 @@ def get_training_level_score(ctx: UmamusumeContext):
     result = []
     for i in range(len(expect_attribute)):
         result.append(expect_attribute[i] / sum(expect_attribute) * total_score)
-    log.debug("每个训练设施的得分：" + str(result))
+    log.debug("每个训练设施的得分: " + str(result))
     return result
 
 
@@ -163,7 +165,26 @@ def get_training_support_card_score(ctx: UmamusumeContext) -> list[float]:
         for j in range(len(turn_info.training_info_list[i].support_card_info_list)):
             score += get_support_card_score(ctx, turn_info.training_info_list[i].support_card_info_list[j])
         result.append(score)
-    log.debug("每个训练的支援卡得分：" + str(result))
+    log.debug("每个训练的支援卡得分: " + str(result))
+    return result
+
+def get_support_card_event_score(ctx: UmamusumeContext) -> list[float]:
+    # 只对URA有效
+    if ctx.task.detail.scenario != ScenarioType.SCENARIO_TYPE_URA:
+        return [0, 0, 0, 0, 0]
+    turn_info = ctx.cultivate_detail.turn_info
+    result = []
+    for i in range (len(turn_info.training_info_list)):
+        has_event = False
+        for j in range(len(turn_info.training_info_list[i].support_card_info_list)):
+            if turn_info.training_info_list[i].support_card_info_list[j].has_event:
+                has_event = True
+                break
+        if has_event:
+            result.append(1*ctx.task.detail.scenario_config.ura_config.getSkillEventWeight(ctx.cultivate_detail.turn_info.date))
+        else:
+            result.append(0)
+    log.debug("每个训练的事件得分: " + str(result))
     return result
 
 

module/umamusume/script/cultivate_task/cultivate.py

@@ -526,6 +526,12 @@ def script_cultivate_learn_skill(ctx: UmamusumeContext):
                         if skill_list[k]["skill_name"] == skill_list[j]["subsequent_skill"]:
                             skill_list[k]["available"] = False
 
+    # 如果是URA, 标记已经学习的技能, 可能会用于重置启发事件权重
+    # 由于上面计算过技能pt，可以保证list里面的技能都会被学到
+    if ctx.task.detail.scenario == ScenarioType.SCENARIO_TYPE_URA:
+        for skill in target_skill_list:
+            ctx.task.detail.scenario_config.ura_config.removeSkillFromList(skill)
+
     # 向上移动至对齐
     ctx.ctrl.swipe(x1=23, y1=950, x2=23, y2=968, duration=100, name="")
     time.sleep(1)

module/umamusume/task.py

@@ -68,7 +68,8 @@ def build_task(task_execute_mode: TaskExecuteMode, task_type: int,
     td.cultivate_result = {}
     # 剧本相关设置
     td.scenario_config = ScenarioConfig(
-        aoharu_config=None if (attachment_data['aoharu_config'] is None) else AoharuConfig(attachment_data['aoharu_config']))
+        ura_config = None if (attachment_data['ura_config'] is None) else UraConfig(attachment_data['ura_config']),
+        aoharu_config = None if (attachment_data['aoharu_config'] is None) else AoharuConfig(attachment_data['aoharu_config']))
     # 限时: 富士奇石的表演秀
     td.fujikiseki_show_mode = attachment_data['fujikiseki_show_mode']
     td.fujikiseki_show_difficulty = attachment_data['fujikiseki_show_difficulty']