diff --git a/README.md b/README.md
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+++ b/README.md
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+TCC Degradation
+-----
+印刷色票の経年劣化（退色）の解析
+
+### 使用方法
+- 環境構築
+```bash
+pip install -r requirements.txt
+```
+- 入力ファイル  
+cm700reader.py を用いて記録したcsvファイルを入力する．経時的に記録したcsvをまとめてフォルダに置いて，データフォルダを指定して実行する．
+
+- 出力ファイル  
+解析結果は3つのcsvファイルと2つのグラフプロット(png)に出力される．
diff --git a/analysis.py b/analysis.py
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+++ b/analysis.py
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+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+csv_file = "out_all.csv"
+stability_file = "out_stability.csv"
+degradation_file = "out_degradation.csv"
+paper_names = {
+    2: "Glossy Pro Platinum",
+    3: "Photo Fine-Grained Luster",
+    4: "Matte Photo",
+    5: "Photo Premium Matte",
+}
+data_dir = r"\\gabor\Project\内科\舌診\TIAS\開発\カラーチャート\TCC2025-01\退色調査"
+
+
+def make_all_data_csv() -> None:
+    """Reads all CSV files."""  # noqa: D401
+
+    print("Combining all data into a single CSV file...")
+    files = [f for f in os.listdir(data_dir) if f.endswith(".csv")]
+    all_data = pd.DataFrame()
+    for i, file in enumerate(files):
+        file_path = os.path.join(data_dir, file)
+        paper = int(file[5:6])
+        datestr = "2025/" + file[7:9] + "/" + file[9:11]
+        iter = 0 if file[11:12] == "a" else 1
+        # print(f"Reading {file_path} (Paper: {paper}, Date: {datestr}, Iteration: {iter})")
+        df = pd.read_csv(file_path, encoding="utf-8")
+        df["tcc"] = df["no"].astype("int")
+        df["paper"] = paper
+        df["date"] = datestr
+        df["iter"] = iter
+        # df.drop(columns=["no", "X", "Y", "Z"], inplace=True, errors="ignore")
+        # df.drop(columns=[f"{c:.1f}" for c in range(400, 710, 10)], inplace=True, errors="ignore")
+        all_data = pd.concat([all_data, df], ignore_index=True)
+    # Save the combined DataFrame to a CSV file
+    all_data[["date", "paper", "iter", "tcc", "L*", "a*", "b*"]].to_csv(
+        csv_file, index=False, encoding="utf-8"
+    )
+
+
+def calc_repeat_stability():
+    """Calculate repeat stability of color measurements."""
+    if not os.path.exists(csv_file):
+        make_all_data_csv()
+
+    print("Calculating repeat stability...")
+    df = pd.read_csv(csv_file, encoding="utf-8", parse_dates=["date"])
+
+    df0 = df[df["iter"] == 0].drop(columns=["iter"]).copy()
+    df0.columns = ["date", "paper", "tcc", "L0", "a0", "b0"]
+    df1 = df[df["iter"] == 1].drop(columns=["iter"]).copy()
+    df1.columns = ["date", "paper", "tcc", "L1", "a1", "b1"]
+    dfm = pd.merge(df0, df1, on=["date", "paper", "tcc"])
+    dfm["dL"] = dfm["L1"] - dfm["L0"]
+    dfm["da"] = dfm["a1"] - dfm["a0"]
+    dfm["db"] = dfm["b1"] - dfm["b0"]
+    dfm["dE"] = np.sqrt(
+        dfm["dL"] ** 2 + dfm["da"] ** 2 + dfm["db"] ** 2
+    )  # Euclidean distance in Lab color space
+    dfm["meanL"] = (dfm["L1"] + dfm["L0"]) / 2
+    dfm["meana"] = (dfm["a1"] + dfm["a0"]) / 2
+    dfm["meanb"] = (dfm["b1"] + dfm["b0"]) / 2
+
+    dfm.to_csv(stability_file, index=False, encoding="utf-8")
+
+    # print("Top 10 largest deltaE values:")
+    # print(dfm.sort_values("dE", ascending=False).head(10))
+
+    # deltaE values Histogram
+    # plt.figure()
+    # dfm["dE"].hist(bins=30)
+    # plt.title("deltaE Histogram")
+    # plt.xlabel("deltaE")
+    # plt.ylabel("Frequency")
+    # plt.show()
+
+
+def color_degradation():
+    """Analyze color degradation over time."""
+    if not os.path.exists(stability_file):
+        calc_repeat_stability()
+
+    print("Calculating color degradation...")
+    dfm = pd.read_csv(stability_file, encoding="utf-8", parse_dates=["date"])
+    dfm = dfm[["date", "paper", "tcc", "meanL", "meana", "meanb"]]
+
+    dfm["changeL"] = 0.0
+    dfm["changea"] = 0.0
+    dfm["changeb"] = 0.0
+    dfm["changeDE"] = 0.0
+    dfm["days"] = 0
+    paper_list = dfm["paper"].drop_duplicates().sort_values()
+    tcc_list = dfm["tcc"].drop_duplicates().sort_values()
+    dfout = pd.DataFrame()
+    for paper in paper_list:
+        paper_data = dfm[dfm["paper"] == paper]
+        begin_date = paper_data["date"].min()
+
+        for tcc in tcc_list:
+            tcc_data = paper_data[paper_data["tcc"] == tcc]
+            begin_lab = (
+                tcc_data[tcc_data["date"] == begin_date][["meanL", "meana", "meanb"]]
+                .iloc[0]
+                .to_list()
+            )
+            tcc_data.loc[:, "days"] = (tcc_data["date"] - begin_date).dt.days
+            tcc_data.loc[:, "changeL"] = tcc_data["meanL"] - begin_lab[0]
+            tcc_data.loc[:, "changea"] = tcc_data["meana"] - begin_lab[1]
+            tcc_data.loc[:, "changeb"] = tcc_data["meanb"] - begin_lab[2]
+            tcc_data.loc[:, "changeDE"] = np.sqrt(
+                tcc_data["changeL"] ** 2 + tcc_data["changea"] ** 2 + tcc_data["changeb"] ** 2
+            )
+            dfout = pd.concat([dfout, tcc_data], ignore_index=True)
+            # print(tcc_data.sort_values("date"))
+            # print("begin_lab", begin_lab)
+        #     break
+        # break
+
+    dfout = dfout.sort_values(by=["paper", "tcc", "date"]).reset_index(drop=True)
+    dfout.to_csv(degradation_file, index=False, encoding="utf-8")
+
+
+def visualize():
+    """Visualize color degradation over time."""
+
+    if not os.path.exists(degradation_file):
+        color_degradation()
+
+    print("Visualizing color degradation...")
+    df = pd.read_csv(degradation_file, encoding="utf-8", parse_dates=["date"])
+    paper_list = df["paper"].drop_duplicates().sort_values()
+
+    fig = plt.figure()
+    fig.set_size_inches(18, 12)
+    fig.suptitle("Color Degradation Over Time (each TCC)", fontsize=16)
+    fig.subplots_adjust(wspace=0.3, hspace=0.4)
+    fig.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95)
+    for i in range(1, 25):
+        ax = fig.add_subplot(4, 6, i)
+        ax.set_title(f"TCC {i}")
+        ax.set_xlabel("Days")
+        ax.set_ylabel("Change in deltaE")
+        for paper in paper_list:
+            paper_data = df[df["paper"] == paper]
+            if not paper_data.empty:
+                ax.plot(
+                    paper_data[paper_data["tcc"] == i]["days"],
+                    paper_data[paper_data["tcc"] == i]["changeDE"],
+                    marker="o",
+                    markersize=4,
+                    label=f"Paper {paper}",
+                )
+                ax.set_ylim(0, 7)  # Set y-axis limit for better visibility
+        ax.legend()
+    fig.savefig("out_degradation_eachTCC.png", dpi=300, bbox_inches="tight")
+
+    paper_mean = df.groupby(["paper", "date"]).mean().reset_index()
+    fig2 = plt.figure()
+    fig2.set_size_inches(9, 6)
+    fig2.suptitle("Color Degradation Over Time (mean TCC)", fontsize=16)
+    for paper in paper_list:
+        paper_data = paper_mean[paper_mean["paper"] == paper]
+        if not paper_data.empty:
+            plt.plot(
+                paper_data["days"],
+                paper_data["changeDE"],
+                marker="o",
+                markersize=6,
+                label=f"Paper {paper} {paper_names[paper]}",
+            )
+    plt.legend(loc="upper left", borderaxespad=0)
+    plt.xlabel("Days")
+    plt.ylabel("Change in deltaE")
+    fig2.savefig("out_degradation_meanTCC.png", dpi=300, bbox_inches="tight")
+
+    # plt.show()
+
+
+if __name__ == "__main__":
+    visualize()
+    print("All done.")
diff --git a/pyproject.toml b/pyproject.toml
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+++ b/pyproject.toml
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+[tool.ruff]
+line-length = 100
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.ruff.lint]
+#select = ["ALL"]
+ignore = [
+    "D1",    # undocumented
+    "D203",  # one blank line before class
+    "D213",  # multi-line summary second line
+    "TD001", # invalid todo tag
+    "TD002", # missing todo author
+    "TD003", # missing todo link
+    "PD011", # pandas use of dot values
+#    "ERA001",
+]
+unfixable = [
+    "F401", # unused import
+    "F841", # unused variable
+]
+
+logger-objects = ["src.library.logger.LOGGER"]
+
+[tool.ruff.lint.pylint]
+max-args = 6
diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+pandas
+matplotlib
+numpy