Cash99/r2

1import pandas as pd2import matplotlib.pyplot as plt3import streamlit as st4import matplotlib as mpl5from io import BytesIO6import numpy as np7 8# 字型設定（繁體中文）9font_path = "SourceHanSansTW-Regular.otf"10mpl.font_manager.fontManager.addfont(font_path)11plt.rcParams['font.family'] = "Source Han Sans TW"12 13# 標題14st.title("📐 R² 傾斜分析工具")15 16# 📘 簡要說明17with st.expander("📘 R² 計算說明", expanded=False):18    st.markdown(r"""19**R²（決定係數）** 是評估資料與趨勢線吻合程度的統計指標，數值範圍為 -∞ 到 1，越接近 1 表示線性趨勢越明顯。20 21其計算公式如下：22 23$$24R^2 = 1 - \frac{SS_{res}}{SS_{tot}}25$$26 27其中：  28- `SS_{res}`：預測殘差平方和（Residual Sum of Squares）  29- `SS_{tot}`：總變異平方和（Total Sum of Squares）30""")31 32# 資料來源選擇33mode = st.radio("請選擇資料來源：", ["手動輸入資料", "上傳 CSV 檔案"])34 35data = None36 37if mode == "手動輸入資料":38    manual_input = st.text_area("請輸入以逗號分隔的數值資料（例：1, 2, 3, 4）", height=100)39    threshold = st.number_input("請輸入判定用的 R² 門檻值", value=0.2, step=0.01)40 41    if st.button("開始分析"):42        try:43            y = [float(i.strip()) for i in manual_input.split(",") if i.strip()]44            if len(y) < 2:45                st.error("❌ 至少需要 2 筆資料")46            else:47                x = list(range(1, len(y) + 1))48                df = pd.DataFrame({"X": x, "Y": y})49                st.write("🔍 資料預覽")50                st.dataframe(df)51 52                # 線性回歸與 R²53                x_np = np.array(x)54                y_np = np.array(y)55                slope, intercept = np.polyfit(x_np, y_np, 1)56                y_pred = slope * x_np + intercept57                ss_total = np.sum((y_np - np.mean(y_np)) ** 2)58                ss_res = np.sum((y_np - y_pred) ** 2)59                r_squared = 1 - (ss_res / ss_total)60 61                # 顯示結果62                st.write(f"📊 R² = `{r_squared:.4f}`")63                st.write(f"📐 斜率 = `{slope:.4f}`")64 65                if r_squared > threshold:66                    st.error("🔺 判斷結果：傾斜")67                else:68                    st.success("✅ 判斷結果：正常")69 70                # 畫圖71                fig, ax = plt.subplots(figsize=(10, 4))72                ax.plot(x, y, 'o-', label='原始資料')73                ax.plot(x, y_pred, '--', color='red', label='趨勢線')74                ax.set_title("資料趨勢圖")75                ax.set_xlabel("X")76                ax.set_ylabel("Y")77                ax.grid(True)78                ax.legend()79                st.pyplot(fig)80        except:81            st.error("❌ 請輸入正確格式的數值（逗號分隔）")82 83else:84    uploaded_file = st.file_uploader("請上傳 CSV 檔案", type=["csv"])85    if uploaded_file:86        try:87            df = pd.read_csv(uploaded_file)88            st.success("✅ 成功讀取 CSV")89            st.write("🔍 資料預覽")90            st.dataframe(df.head())91 92            numeric_cols = df.select_dtypes(include="number").columns.tolist()93            if len(numeric_cols) < 2:94                st.warning("❗ 檔案中需至少包含兩個數值欄位")95            else:96                x_col = st.selectbox("請選擇 X 軸欄位", options=numeric_cols)97                y_col = st.selectbox("請選擇 Y 軸欄位", options=numeric_cols)98                threshold = st.number_input("請輸入判定用的 R² 門檻值", value=0.2, step=0.01)99 100                if st.button("開始分析"):101                    x = df[x_col].values102                    y = df[y_col].values103                    slope, intercept = np.polyfit(x, y, 1)104                    y_pred = slope * x + intercept105                    ss_total = np.sum((y - np.mean(y)) ** 2)106                    ss_res = np.sum((y - y_pred) ** 2)107                    r_squared = 1 - (ss_res / ss_total)108 109                    st.write(f"📊 R² = `{r_squared:.4f}`")110                    st.write(f"📐 斜率 = `{slope:.4f}`")111 112                    if r_squared > threshold:113                        st.error("🔺 判斷結果：傾斜")114                    else:115                        st.success("✅ 判斷結果：正常")116 117                    # 畫圖118                    fig, ax = plt.subplots(figsize=(10, 4))119                    ax.scatter(x, y, label="原始資料", color="#0072B2")120                    ax.plot(x, y_pred, '--', color='red', label='趨勢線')121                    ax.set_title("資料趨勢圖")122                    ax.set_xlabel(x_col)123                    ax.set_ylabel(y_col)124                    ax.grid(True)125                    ax.legend()126                    st.pyplot(fig)127        except Exception as e:128            st.error(f"❌ 讀取錯誤：{e}")129