remove archive

archive/app.py

@@ -1,42 +0,0 @@
-from typing import Any
-
-import gradio as gr
-
-from archive.utils import get_calculated_df, get_info, text2oddslist
-
-DEFAULT_AMOUNT = 5000
-
-
-def calculate(_amount: Any, _text: Any) -> tuple[str, dict]:
-    amount = int(_amount) or DEFAULT_AMOUNT
-
-    df = get_calculated_df(amount, text2oddslist(str(_text)))
-    info = get_info(df)
-
-    # dfの整形
-    display_cols = ["odds", "buy", "refound"]
-    if df["name"].map(lambda name: name != "").any():
-        display_cols = ["name"] + display_cols
-
-    # markdownの整形
-    md_string = (
-        f"**購入額:** ¥ **{info['sum']:,}**<br>"
-        f"**点数:** **{info['num_kind']}** 点<br>"
-        f"**払戻:** ¥ **{info['refound_mean']:,}** (**{info['profit_mean']:+,}** (**{info['rate_min']:+.0%}**))<br>"
-        "---"
-    )
-
-    return md_string, df[["index"] + display_cols]
-
-
-amount_input = gr.Number(value=DEFAULT_AMOUNT, label="amount")
-text_input = gr.Textbox(label="text")
-markdown_output = gr.Markdown()
-df_output = gr.Dataframe(type="pandas")
-iface = gr.Interface(
-    fn=calculate,
-    inputs=[amount_input, text_input],
-    outputs=[markdown_output, df_output],
-)
-
-iface.launch()

archive/app_gradio.py

@@ -1,39 +0,0 @@
-import gradio as gr
-from archive.utils import get_calculated_df, get_info, text2oddslist
-
-DEFAULT_AMOUNT = 5000
-
-
-def calculate(amount, text):
-    amount = amount or DEFAULT_AMOUNT
-    odds_list = text2oddslist(text)
-    df = get_calculated_df(amount, odds_list)
-    info = get_info(df)
-
-    # dfの整形
-    display_cols = ["odds", "buy", "refound"]
-    if df["name"].map(lambda name: name != "").any():
-        display_cols = ["name"] + display_cols
-
-    # markdownの整形
-    md_string = (
-        f"**購入額:** ¥ **{info['sum']:,}**<br>"
-        f"**点数:** **{info['num_kind']}** 点<br>"
-        f"**払戻:** ¥ **{info['refound_mean']:,}** (**{info['profit_mean']:+,}** (**{info['rate_min']:+.0%}**))<br>"
-        "---"
-    )
-
-    return md_string, df[["index"] + display_cols]
-
-
-amount_input = gr.Number(value=DEFAULT_AMOUNT, label="amount")
-text_input = gr.Textbox(label="text")
-markdown_output = gr.Markdown()
-df_output = gr.Dataframe(type="pandas")
-iface = gr.Interface(
-    fn=calculate,
-    inputs=[amount_input, text_input],
-    outputs=[markdown_output, df_output],
-)
-
-iface.launch()

archive/app_streamlit.py

@@ -1,28 +0,0 @@
-import streamlit as st
-
-from archive.utils import get_calculated_df, get_info, text2oddslist
-
-amount: int = int(st.number_input("amount", min_value=100, value=3000, step=100))
-text: str = st.text_area("text", "")
-# ボタンを作る
-st.button("calculate")
-if text:
-    df = get_calculated_df(amount, text2oddslist(text))
-    # dataframeを綺麗に出力する
-    info = get_info(df)
-    col1, col2 = st.columns(2)
-    with col1:
-        st.markdown(f"#### 購入額: ¥ **{info['sum']:,}**")
-        st.markdown(f"#### 点　数: **{info['num_kind']}** 点")
-    with col2:
-        st.metric(
-            label="払戻",
-            value=f"¥ {info['refound_mean']:,}",
-            delta=f"{info['profit_mean']:+,}({info['rate_min']:.0%})",
-        )
-    st.markdown("---")
-    # dfの表示
-    display_cols = ["odds", "buy", "refound"]
-    if df["name"].map(lambda name: name != "").any():
-        display_cols = ["name"] + display_cols
-    st.dataframe(df[display_cols])

archive/utils.py

@@ -1,107 +0,0 @@
-import math
-from typing import TypedDict
-
-import pandas as pd
-
-
-# Typing -----------------------------------------------------------------------
-class OddsInfo(TypedDict):
-    index: str
-    name: str
-    odds: float
-
-
-OdddsList = list[OddsInfo]
-
-
-class Info(TypedDict):
-    sum: int
-    num_kind: int
-    refound_mean: int
-    profit_mean: int
-    rate_min: float
-    rate_max: float
-    rate_mean: float
-
-
-# text　->　odds_list ------------------------------------------------------------
-def _text2oddslist_pc(text: str) -> OdddsList:
-    odds_list: OdddsList = []
-    tmp_list: list[str] = []
-    lines: list[str] = text.split("\n")
-    for text in lines:
-        if "." in text:
-            odds_list.append(
-                {
-                    "index": "-".join(tmp_list),
-                    "name": "",
-                    "odds": float(text),
-                }
-            )
-            tmp_list = []
-        else:
-            if text.strip():
-                tmp_list.append(text.strip())
-    return odds_list
-
-
-def _text2oddslist_mobile(text: str) -> OdddsList:
-    odds_list: OdddsList = []
-    lines: list[str] = text.split("\n")
-    for strat_key in ("選択", "オッズ", "組み合わせ", "オッズを見て個別選択"):
-        if strat_key in lines:
-            lines = lines[lines.index(strat_key) + 1 :]
-    if "すべてを選択" in lines:
-        lines = lines[: lines.index("すべてを選択")]
-    for i in range(len(lines) // 3):
-        # oddsが1つ目にある
-        if "." in lines[3 * i + 1]:
-            idx_odds = 1
-            idx_name = 2
-        else:
-            idx_odds = 2
-            idx_name = 1
-
-        odds_list.append(
-            {
-                "index": "-".join(lines[3 * i].split()),
-                "name": "-".join(lines[3 * i + idx_name].split()),
-                "odds": float(lines[3 * i + idx_odds]),
-            }
-        )
-    return odds_list
-
-
-def text2oddslist(text: str) -> OdddsList:
-    # pcの時
-    if set("".join(text.split())) <= set("0123456789.-"):
-        odds_list = _text2oddslist_pc(text)
-    # moduleの時
-    else:
-        odds_list = _text2oddslist_mobile(text)
-    return odds_list
-
-
-# odds_list -> df ---------------------------------------------------------------
-def get_calculated_df(amount: int, odds_list: OdddsList) -> pd.DataFrame:
-    df = pd.DataFrame(odds_list)
-    df["weight"] = (1 / df["odds"]) / (1 / df["odds"]).sum()
-    df["buy"] = (df["weight"] * amount / 100).map(
-        lambda x: max(math.floor(x) * 100, 100)
-    )
-    df["refound"] = (df["buy"] * df["odds"]).astype(int)
-    df["profit"] = df["refound"] - amount
-    return df
-
-
-def get_info(df: pd.DataFrame) -> Info:
-    info: Info = {
-        "sum": df["buy"].sum(),
-        "num_kind": df.shape[0],
-        "refound_mean": int(df["refound"].mean()),
-        "profit_mean": int(df["profit"].mean()),
-        "rate_min": df["refound"].min() / df["buy"].sum(),
-        "rate_max": df["refound"].max() / df["buy"].sum(),
-        "rate_mean": df["refound"].mean() / df["buy"].sum(),
-    }
-    return info