feat: update

app.py

@@ -1,19 +1,39 @@
 import streamlit as st
-import sample
+import rcity_tenho_sampling
+
+# import yonma_sample
 
 st.set_page_config(layout="wide")
-st.title("rcity 10dan saka Sampling")
+st.title("tenho or rcity 10dan saka Sampling")
 
 num_games = st.selectbox("Number of games", [100, 500, 1000, 2000, 3000], index=1)
 num_dan = st.selectbox("Number of dan", [7, 8, 9, 10], index=3)
+platform = st.selectbox("platform", ("rcity", "tenhou"), key="platform")
+print(f"platform: {platform}, enum: {rcity_tenho_sampling.Platform[platform]}")
 num_max_pt = st.selectbox("Number of max points", [9000, 10000, 15000, 20000], index=3)
+col1, col2, col3 = st.columns(3)
+with col1:
+    custom_1st_p = st.number_input("1st rate(custom)", min_value=0.0, max_value=1.0)
+with col2:
+    custom_2nd_p = st.number_input("2nd rate(custom)", min_value=0.0, max_value=1.0)
+with col3:
+    custom_3rd_p = st.number_input("3rd rate(custom)", min_value=0.0, max_value=1.0)
+custom_rates = None
+if custom_1st_p + custom_2nd_p + custom_3rd_p == 1.0:
+    st.info("custom rating enabled")
+    custom_rates = [custom_1st_p, custom_2nd_p, custom_3rd_p]
+print(f"1st: {custom_1st_p}, 2nd: {custom_2nd_p}, 3rd: {custom_3rd_p}")
 if num_games is None or num_dan is None:
     st.stop()
-df = sample.readcsv(filter=num_dan)
+df = rcity_tenho_sampling.readcsv(
+    filter=num_dan, platform=rcity_tenho_sampling.Platform[platform]
+)
 if df is not None:
     if st.button("再計算") or num_games is not None:
         col1, col2 = st.columns(2)
-        plt = sample.simulate_games(df, num_games=num_games, max_score=num_max_pt)
+        plt = rcity_tenho_sampling.simulate_games(
+            df, num_games=num_games, max_score=num_max_pt, custom_rates=custom_rates
+        )
         with col1:
             st.pyplot(plt)
         with col2:

rcity_sanma.csv

@@ -0,0 +1,17 @@
+dan,place,win,draw,lose,win_rate,draw_rate,lose_rate,init_score,rank_up_score
+10,enton,90,0,-120,0.3506,0.3742,0.2752,3800,7600
+10,enhan,135,0,-180,0.35,0.38,0.30,3800,7600
+10,ginton,160,0,-180,0.3636,0.3322,0.3042,3800,7600
+10,ginhan,240,0,-270,0.3481,0.3754,0.2827,3800,7600
+7,ginton,160,0,-150,0.35,0.38,0.27,2300,4600
+8,ginton,160,0,-160,0.35,0.38,0.27,2800,5600
+9,ginton,160,0,-170,0.35,0.38,0.27,3300,6600
+7,ginhan,240,0,-225,0.35,0.38,0.27,2300,4600
+8,ginhan,240,0,-240,0.35,0.38,0.27,2800,5600
+9,ginhan,240,0,-255,0.35,0.38,0.27,3300,6600
+7,enton,90,0,-90,0.35,0.38,0.27,2300,4600
+8,enton,90,0,-100,0.35,0.38,0.27,2800,5600
+9,enton,90,0,-110,0.35,0.38,0.27,3300,6600
+7,enhan,135,0,-135,0.35,0.38,0.27,2300,4600
+8,enhan,135,0,-150,0.35,0.38,0.27,2800,5600
+9,enhan,135,0,-165,0.35,0.38,0.27,3300,6600

rcity_tenho_sampling.py

@@ -0,0 +1,185 @@
+import matplotlib.pyplot
+import pandas as pd
+import os
+import random
+import enum
+import pydantic
+
+
+def main():
+    df = readcsv(10)
+    if df is not None:
+        plt = simulate_games(df, num_games=2000)
+        plt.show()
+
+
+class Platform(enum.IntEnum):
+    rcity = 1
+    tenhou = 2
+
+
+def readcsv(
+    filter: int = 10,
+    platform: Platform = Platform.rcity,
+) -> pd.DataFrame | None:
+    # CSVファイルの存在確認
+    if platform == Platform.rcity:
+        csv_file = "rcity_sanma.csv"
+    elif platform == Platform.tenhou:
+        csv_file = "tenho_sanma.csv"
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    csv_path = os.path.join(current_dir, csv_file)
+    if not os.path.isfile(csv_path):
+        print(f"Error: {csv_file} does not exist.")
+        return None
+
+    # CSVファイルの読み込み
+    df = pd.read_csv(csv_path)
+
+    filtered_df = df[df.iloc[:, 0] == filter]
+
+    # フィルター後のデータフレームを表示
+    print(filtered_df)
+    return filtered_df
+
+
+def simulate_games(
+    df,
+    num_games: int = 1000,
+    max_score: int = 20000,
+    custom_rates: None | list[int, int, int] = None,
+):
+    results = {}
+
+    for index, row in df.iterrows():
+        place = row["place"]
+        win_score = row["win"]
+        lose_score = row["lose"]
+        draw_score = row["draw"]
+        win_rate = row["win_rate"]
+        lose_rate = row["lose_rate"]
+        draw_rate = row["draw_rate"]
+        init_score = row["init_score"]
+        if custom_rates is not None:
+            win_rate, lose_rate, draw_rate = custom_rates
+        scores = [init_score]
+        for _ in range(num_games):
+            result = random.choices(
+                [1, 2, 3], weights=[win_rate, lose_rate, draw_rate]
+            )[0]
+            if result == 1:
+                scores.append(scores[-1] + win_score)
+            elif result == 2:
+                scores.append(scores[-1] + lose_score)
+            else:
+                scores.append(scores[-1] + draw_score)
+
+        results[place] = scores
+        df.at[index, "reached_goal"] = any(
+            [score >= row["rank_up_score"] for score in scores]
+        )
+        df.at[index, "rank_down"] = any([score <= 0 for score in scores])
+
+    matplotlib.pyplot.figure(figsize=(10, 6))
+    for place, scores in results.items():
+        matplotlib.pyplot.plot(range(num_games + 1), scores, label=place)
+    matplotlib.pyplot.axhline(
+        y=df.iloc[0]["init_score"], color="black", linestyle="--", label="initial score"
+    )
+    matplotlib.pyplot.axhline(
+        y=df.iloc[0]["rank_up_score"], color="red", linestyle="--", label="goal"
+    )
+    matplotlib.pyplot.xlabel("Game")
+    matplotlib.pyplot.ylabel("Score")
+    matplotlib.pyplot.ylim(0, max_score)
+    matplotlib.pyplot.title("Score Transition")
+    matplotlib.pyplot.legend()
+    return matplotlib.pyplot
+
+
+class Result(pydantic.BaseModel):
+    place: str
+    is_reached_goal: bool
+    is_koudan: bool
+
+
+def simulate_games_core(
+    df,
+    num_games: int = 1000,
+    custom_rates: None | list[int, int, int] = None,
+) -> list[Result]:
+    results = []
+
+    for _, row in df.iterrows():
+        place = row["place"]
+        win_score = row["win"]
+        lose_score = row["lose"]
+        draw_score = row["draw"]
+        win_rate = row["win_rate"]
+        lose_rate = row["lose_rate"]
+        draw_rate = row["draw_rate"]
+        init_score = row["init_score"]
+        rank_up_score = row["rank_up_score"]
+
+        if custom_rates is not None:
+            win_rate, lose_rate, draw_rate = custom_rates
+
+        score = init_score
+        is_reached_goal = False
+        is_koudan = False
+
+        for _ in range(num_games):
+            result = random.choices(
+                [1, 2, 3], weights=[win_rate, lose_rate, draw_rate]
+            )[0]
+            if result == 1:
+                score += win_score
+            elif result == 2:
+                score += lose_score
+            else:
+                score += draw_score
+
+            if score >= rank_up_score:
+                is_reached_goal = True
+                break
+            if score <= 0:
+                is_koudan = True
+                break
+
+        results.append(
+            {"place": place, "is_reached_goal": is_reached_goal, "is_koudan": is_koudan}
+        )
+
+    return results
+
+
+def testing(num: int = 20):
+    """
+    simulate_gamesをn回回して、結果を表示する
+    """
+    df = readcsv(10)
+
+    if df is not None:
+        place_stats = {}
+
+        for _ in range(num):
+            results = simulate_games_core(df, num_games=2000)
+            for res in results:
+                place = res["place"]
+                if place not in place_stats:
+                    place_stats[place] = {"promotions": [], "demotions": []}
+                place_stats[place]["promotions"].append(res["is_reached_goal"])
+                place_stats[place]["demotions"].append(res["is_koudan"])
+
+        for place, stats in place_stats.items():
+            print("-----")
+            print(f"place: {place}")
+            print(f"回した回数: {num}")
+            print(f"昇段: {stats['promotions']}")
+            print(f"後段: {stats['demotions']}")
+            print(f"合計昇段回数: {sum(stats['promotions'])}")
+            print(f"合計後段回数: {sum(stats['demotions'])}")
+
+
+if __name__ == "__main__":
+    testing()

tenho_sanma.csv

@@ -0,0 +1,41 @@
+dan,place,win,draw,lose,win_rate,draw_rate,lose_rate,init_score,rank_up_score
+1,basic_nan,45,0,-30,0.3481,0.3754,0.2827,200,400
+2,basic_nan,45,0,-60,0.3481,0.3754,0.2827,400,800
+3,basic_nan,45,0,-75,0.3481,0.3754,0.2827,600,1200
+4,basic_nan,45,0,-90,0.3481,0.3754,0.2827,800,1600
+5,basic_nan,45,0,-105,0.3481,0.3754,0.2827,1000,2000
+6,basic_nan,45,0,-120,0.3481,0.3754,0.2827,1200,2400
+7,basic_nan,45,0,-135,0.3481,0.3754,0.2827,1400,2800
+8,basic_nan,45,0,-150,0.3481,0.3754,0.2827,1600,3200
+9,basic_nan,45,0,-165,0.3481,0.3754,0.2827,1800,3600
+10,basic_nan,45,0,-180,0.3481,0.3754,0.2827,2000,4000
+1,joukyu_nan,75,0,-30,0.3481,0.3754,0.2827,200,400
+2,joukyu_nan,75,0,-60,0.3481,0.3754,0.2827,400,800
+3,joukyu_nan,75,0,-75,0.3481,0.3754,0.2827,600,1200
+4,joukyu_nan,75,0,-90,0.3481,0.3754,0.2827,800,1600
+5,joukyu_nan,75,0,-105,0.3481,0.3754,0.2827,1000,2000
+6,joukyu_nan,75,0,-120,0.3481,0.3754,0.2827,1200,2400
+7,joukyu_nan,75,0,-135,0.3481,0.3754,0.2827,1400,2800
+8,joukyu_nan,75,0,-150,0.3481,0.3754,0.2827,1600,3200
+9,joukyu_nan,75,0,-165,0.3481,0.3754,0.2827,1800,3600
+10,joukyu_nan,75,0,-180,0.3481,0.3754,0.2827,2000,4000
+1,tokujou_nan,105,0,-30,0.3481,0.3754,0.2827,200,400
+2,tokujou_nan,105,0,-60,0.3481,0.3754,0.2827,400,800
+3,tokujou_nan,105,0,-75,0.3481,0.3754,0.2827,600,1200
+4,tokujou_nan,105,0,-90,0.3481,0.3754,0.2827,800,1600
+5,tokujou_nan,105,0,-105,0.3481,0.3754,0.2827,1000,2000
+6,tokujou_nan,105,0,-120,0.3481,0.3754,0.2827,1200,2400
+7,tokujou_nan,105,0,-135,0.3481,0.3754,0.2827,1400,2800
+8,tokujou_nan,105,0,-150,0.3481,0.3754,0.2827,1600,3200
+9,tokujou_nan,105,0,-165,0.3481,0.3754,0.2827,1800,3600
+10,tokujou_nan,105,0,-180,0.3481,0.3754,0.2827,2000,4000
+1,houou_nan,135,0,-30,0.3481,0.3754,0.2827,200,400
+2,houou_nan,135,0,-60,0.3481,0.3754,0.2827,400,800
+3,houou_nan,135,0,-75,0.3481,0.3754,0.2827,600,1200
+4,houou_nan,135,0,-90,0.3481,0.3754,0.2827,800,1600
+5,houou_nan,135,0,-105,0.3481,0.3754,0.2827,1000,2000
+6,houou_nan,135,0,-120,0.3481,0.3754,0.2827,1200,2400
+7,houou_nan,135,0,-135,0.3481,0.3754,0.2827,1400,2800
+8,houou_nan,135,0,-150,0.3481,0.3754,0.2827,1600,3200
+9,houou_nan,135,0,-165,0.3481,0.3754,0.2827,1800,3600
+10,houou_nan,135,0,-180,0.3481,0.3754,0.2827,2000,4000