diff --git a/analysis.py b/analysis.py
index 0349e53..deb3a03 100644
--- a/analysis.py
+++ b/analysis.py
@@ -20,7 +20,10 @@
     "5A": "ELECOM Matte Photo (open)",
     "5B": "ELECOM Matte Photo (close)",
 }
-data_dir = r"D:\usr\prog\CM700Reader"
+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3 マットフォト" + "\\"
+)
+colors = ["tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple"]
 
 
 def make_all_data_csv() -> None:
@@ -35,6 +38,7 @@
         file_path = os.path.join(data_dir, file)
         parts = os.path.splitext(os.path.basename(file))[0].split("_")
         paper = parts[0][5:7]
+        # paper = "4B"  # 紙種類固定
         datestr = parts[1] + "-" + parts[2]
         # iter = 0 if file[11:12] == "a" else 1
         iter = 0
@@ -173,16 +177,17 @@
     for i in range(1, 25):
         ax = fig.add_subplot(4, 6, i)
         ax.set_title(f"TCC {i}")
-        ax.set_xlabel("Minutes")
+        ax.set_xlabel("Days")
         ax.set_ylabel("Change in deltaE")
-        for paper in paper_list:
+        for pidx, paper in enumerate(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]["days"] / (24 * 60),
                     paper_data[paper_data["tcc"] == i]["changeDE"],
-                    marker="o",
+                    marker="o" if (pidx % 2) == 0 else "^",
                     markersize=4,
+                    color=colors[pidx // 2],
                     label=f"Paper {paper}",
                 )
                 # ax.set_ylim(0, 3)  # Set y-axis limit for better visibility
@@ -193,18 +198,19 @@
     fig2 = plt.figure()
     fig2.set_size_inches(9, 6)
     fig2.suptitle("Color Degradation Over Time (mean TCC)", fontsize=16)
-    for paper in paper_list:
+    for pidx, paper in enumerate(paper_list):
         paper_data = paper_mean[paper_mean["paper"] == paper]
         if not paper_data.empty:
             plt.plot(
-                paper_data["days"],
+                paper_data["days"] / (24 * 60),
                 paper_data["changeDE"],
-                marker="o",
+                marker="o" if (pidx % 2) == 0 else "^",
                 markersize=6,
+                color=colors[pidx // 2],
                 label=f"Paper {paper} {paper_names[paper]}",
             )
     plt.legend(loc="upper left", borderaxespad=0)
-    plt.xlabel("Minutes since first measurement")
+    plt.xlabel("Days since first measurement")
     plt.ylabel("Change in deltaE")
     fig2.savefig("out_degradation_meanTCC.png", dpi=300, bbox_inches="tight")
 
@@ -212,17 +218,17 @@
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Analyze and visualize color degradation.")
-    parser.add_argument(
-        "csvfile",
-        help="Measured CSV file paths",
-    )
-    args = parser.parse_args()
-    if not args.csvfile.endswith(".csv"):
-        print("Please provide the path to the CSV file.")
-        exit(1)
-    print("datadir:", os.path.dirname(args.csvfile))
-    data_dir = os.path.dirname(args.csvfile)
+    # parser = argparse.ArgumentParser(description="Analyze and visualize color degradation.")
+    # parser.add_argument(
+    #     "csvfile",
+    #     help="Measured CSV file paths",
+    # )
+    # args = parser.parse_args()
+    # if not args.csvfile.endswith(".csv"):
+    #     print("Please provide the path to the CSV file.")
+    #     exit(1)
+    # print("datadir:", os.path.dirname(args.csvfile))
+    # data_dir = os.path.dirname(args.csvfile)
 
     print("Starting analysis...")
     visualize()
diff --git a/print_check.py b/print_check.py
new file mode 100644
index 0000000..1d78496
--- /dev/null
+++ b/print_check.py
@@ -0,0 +1,60 @@
+import os
+import re
+
+import pandas as pd
+
+measure_dir = (
+    r"\\gabor\Project\内科\舌診\TIAS\開発\カラーチャート\TCC2025-01\退色調査3 マットフォト" + "\\"
+)
+target_lab_csv = (
+    r"\\gabor\Project\内科\舌診\TIAS\開発\カラーチャート\TCC2025-01\色調整\Target_Lab.csv"
+)
+tcc_lab = []
+
+
+def read_data():
+    """Read data files and check for required columns."""
+
+    global tcc_lab
+    kinds = ["tcc1", "tcc2", "tcc3", "tcc4", "tcc5", "tcc6"]
+    for i in range(24):
+        tcc_lab.append([])
+
+    target_lab = pd.read_csv(target_lab_csv, encoding="utf-8", header=None)
+
+    files = []
+    for kind in kinds:
+        csv_files = [
+            f for f in os.listdir(measure_dir) if f.endswith(".csv") and f.startswith(kind)
+        ]
+        csv_files.sort(reverse=True)
+        files.append(csv_files[0])  # Get the latest file for each kind
+
+    for file in files:
+        file_path = os.path.join(measure_dir, file)
+        print(f"Reading {file}")
+        df = pd.read_csv(file_path, encoding="utf-8")
+        required_columns = {"no", "L*", "a*", "b*"}
+        if not required_columns.issubset(df.columns):
+            print(f"Error: Missing required columns in {file_path}")
+            exit(1)
+        for index, row in df.iterrows():
+            no = int(row["no"])
+            L = float(row["L*"])
+            a = float(row["a*"])
+            b = float(row["b*"])
+            tcc_lab[no - 1].append((L, a, b))
+
+    for index, row in target_lab.iterrows():
+        print(f"CC{index + 1:02d} Target Lab: L={row[0]}, a={row[1]}, b={row[2]}")
+        for i in range(len(tcc_lab[index])):
+            L, a, b = tcc_lab[index][i]
+            print(
+                f"  Measured in {kinds[i]}: L={L}, a={a}, b={b} "
+                f"(ΔE*={((L - row[0]) ** 2 + (a - row[1]) ** 2 + (b - row[2]) ** 2) ** 0.5:.2f})"
+            )
+    # print(tcc_lab)
+
+
+if __name__ == "__main__":
+    read_data()