libs.commands.results.versus

  1"""
  2libs/commands/results/versus.py
  3"""
  4
  5import textwrap
  6
  7import pandas as pd
  8
  9import libs.global_value as g
 10from libs.data import loader
 11from libs.functions import message
 12from libs.utils import formatter
 13
 14
 15def aggregation() -> tuple[str, dict, dict]:
 16    """直接対戦結果を集計して返す
 17
 18    Returns:
 19        tuple[str, dict, dict]
 20        - str: ヘッダ情報
 21        - dict: 集計データ
 22        - dict: 生成ファイル情報
 23    """
 24
 25    # 検索動作を合わせる
 26    g.params.update(guest_skip=g.params.get("guest_skip2"))
 27
 28    # --- データ収集
 29    df_vs = loader.read_data("summary/versus_matrix.sql")
 30    df_game = loader.read_data("summary/details.sql").fillna(value="")
 31    df_data = pd.DataFrame(columns=df_game.columns)  # ファイル出力用
 32
 33    my_name = formatter.name_replace(g.params["player_name"], add_mark=True)
 34    vs_list = [formatter.name_replace(x, add_mark=True) for x in g.params["competition_list"].values()]
 35
 36    # --- 匿名化
 37    if g.params.get("anonymous"):
 38        mapping_dict = formatter.anonymous_mapping([my_name] + vs_list)
 39        my_name = mapping_dict[my_name]
 40        vs_list = [mapping_dict[name] for name in vs_list]
 41        df_vs["my_name"] = df_vs["my_name"].replace(mapping_dict)
 42        df_vs["vs_name"] = df_vs["vs_name"].replace(mapping_dict)
 43
 44    # --- 表示内容
 45    if g.params.get("all_player"):
 46        vs = "全員"
 47    else:
 48        vs = ",".join(vs_list)
 49
 50    msg1 = tmpl_header(my_name, vs)
 51    msg2: dict = {}  # 対戦結果格納用
 52
 53    tmp_msg: dict = {}
 54    drop_name: list = []  # 対戦記録なしプレイヤー
 55    if len(df_vs) == 0:  # 検索結果なし
 56        msg2[""] = "対戦記録が見つかりません。\n"
 57        return (msg1, msg2, {})
 58
 59    for vs_name in vs_list:
 60        tmp_msg[vs_name] = {}
 61        if vs_name in vs_list:
 62            data = df_vs.query("my_name == @my_name and vs_name == @vs_name")
 63            if data.empty:
 64                drop_name.append(vs_name)
 65                tmp_msg[vs_name]["info"] = f"*【{my_name} vs {vs_name}】*\n\t対戦記録はありません。"
 66                continue
 67
 68            tmp_msg[vs_name]["info"] = tmpl_vs_table(data.to_dict(orient="records")[0])
 69
 70            # ゲーム結果
 71            if g.params.get("game_results"):
 72                count = 0
 73                my_score = df_game.query("name == @my_name")
 74                vs_score = df_game.query("name == @vs_name")
 75                my_playtime = my_score["playtime"].to_list()
 76                vs_playtime = vs_score["playtime"].to_list()
 77
 78                for playtime in sorted(set(my_playtime + vs_playtime)):
 79                    if playtime in my_playtime and playtime in vs_playtime:
 80                        current_game = df_game.query("playtime == @playtime")
 81                        df_data = current_game if df_data.empty else pd.concat([df_data, current_game])
 82                        count += 1
 83        else:  # 対戦記録なし
 84            tmp_msg[vs_name]["info"] = f"*【{my_name} vs {vs_name}】*\n\t対戦相手が見つかりません。\n\n"
 85
 86    # --- データ整列&まとめ
 87    for key, val in tmp_msg.items():
 88        if key in drop_name and len(vs_list) > 5 and not g.params.get("all_player"):
 89            continue
 90        msg2[f"{key}_info"] = val.pop("info") + "\n"
 91        if val:
 92            for x in val:
 93                msg2[f"{key}_{x}"] = textwrap.indent(val[x], "\t") + "\n"
 94
 95    # --- ファイル出力
 96    if len(df_data) != 0:
 97        df_data["座席"] = df_data["seat"].apply(lambda x: ["東家", "南家", "西家", "北家"][x - 1])
 98        df_data["rpoint"] = df_data["rpoint"] * 100
 99    df_data = formatter.df_rename(
100        df_data.filter(items=["playtime", "座席", "name", "rank", "rpoint", "point", "grandslam"]).drop_duplicates(),
101        short=False
102    )
103
104    namelist = list(g.params["competition_list"].values())  # pylint: disable=unused-variable  # noqa: F841
105    df_vs["対戦相手"] = df_vs["vs_name"].apply(lambda x: str(x).strip())
106    df_vs["my_rpoint_avg"] = (df_vs["my_rpoint_avg"] * 100).astype("int")
107    df_vs["vs_rpoint_avg"] = (df_vs["vs_rpoint_avg"] * 100).astype("int")
108    df_vs = formatter.df_rename(df_vs)
109    df_vs2 = df_vs.query("vs_name == @namelist").filter(
110        items=[
111            "対戦相手", "対戦結果", "勝率",
112            "獲得ポイント(自分)", "平均ポイント(自分)", "平均素点(自分)", "順位分布(自分)", "平均順位(自分)",
113            "獲得ポイント(相手)", "平均ポイント(相手)", "平均素点(相手)", "順位分布(相手)", "平均順位(相手)",
114        ]
115    ).drop_duplicates()
116
117    match str(g.params.get("format", "default")).lower():
118        case "csv":
119            file_list = {
120                "対戦結果": formatter.save_output(df_data, "csv", "result.csv"),
121                "成績": formatter.save_output(df_vs2, "csv", "versus.csv"),
122            }
123        case "text" | "txt":
124            file_list = {
125                "対戦結果": formatter.save_output(df_data, "txt", "result.txt"),
126                "成績": formatter.save_output(df_vs2, "txt", "versus.txt"),
127            }
128        case _:
129            file_list = {}
130
131    return (msg1, msg2, file_list)
132
133
134def tmpl_header(my_name: str, vs_name: str) -> str:
135    """ヘッダテンプレート
136
137    Args:
138        my_name (str): 自分の名前
139        vs_name (str): 相手の名前
140
141    Returns:
142        str: 出力データ
143    """
144    ret = textwrap.dedent(
145        f"""\
146        *【直接対戦結果】*
147        \tプレイヤー名：{my_name}
148        \t対戦相手：{vs_name}
149        \t{message.item_search_range()}
150        \t{message.remarks(True)}
151        """
152    ).strip()
153
154    return message.del_blank_line(ret)
155
156
157def tmpl_vs_table(data: dict) -> str:
158    """直接対決結果表示テンプレート
159
160    Args:
161        data (dict): 結果データ
162
163    Returns:
164        str: 出力データ
165    """
166
167    ret = f"*【{data["my_name"].strip()} vs {data["vs_name"].strip()}】*\n"
168    ret += textwrap.indent(
169        "".join([
170            textwrap.dedent(
171                f"""\
172                対戦数：{data["game"]} 戦 {data["win"]} 勝 {data["lose"]} 敗 ({data["win%"]:.2f}%)
173                平均素点差：{(data["my_rpoint_avg"] - data["vs_rpoint_avg"]) * 100:+.0f} 点
174                獲得ポイント合計(自分)：{data["my_point_sum"]:+.1f}pt
175                獲得ポイント合計(相手)：{data["vs_point_sum"]:+.1f}pt
176                """.replace("-", "▲")
177            ),
178            textwrap.dedent(
179                f"""\
180                順位分布(自分)：{data["my_1st"]}-{data["my_2nd"]}-{data["my_3rd"]}-{data["my_4th"]} ({data["my_rank_avg"]:1.2f})
181                順位分布(相手)：{data["vs_1st"]}-{data["vs_2nd"]}-{data["vs_3rd"]}-{data["vs_4th"]} ({data["vs_rank_avg"]:1.2f})
182                """
183            )
184        ]),
185        "\t"
186    )
187
188    return ret.strip() + "\n"