diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..80a85ad
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+*.csv
+*.png
diff --git a/analysis.py b/analysis.py
index b1fbfdb..7fb3ead 100644
--- a/analysis.py
+++ b/analysis.py
@@ -13,7 +13,7 @@
     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"
+data_dir = r"D:\usr\prog\CM700Reader"
 
 
 def make_all_data_csv() -> None:
@@ -23,10 +23,14 @@
     files = [f for f in os.listdir(data_dir) if f.endswith(".csv")]
     all_data = pd.DataFrame()
     for i, file in enumerate(files):
+        if not file.startswith("dobot"):
+            continue
         file_path = os.path.join(data_dir, file)
-        paper = int(file[5:6])
-        datestr = "2025/" + file[7:9] + "/" + file[9:11]
+        parts = os.path.splitext(os.path.basename(file))[0].split("_")
+        paper = 2
+        datestr = parts[1] + "-" + parts[2]
         iter = 0 if file[11:12] == "a" else 1
+        iter = 0
         # 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")
@@ -36,6 +40,7 @@
         # 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)
+    all_data.sort_values(by=["date", "paper", "iter", "tcc"], inplace=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"
@@ -50,20 +55,35 @@
     print("Calculating repeat stability...")
     df = pd.read_csv(csv_file, encoding="utf-8", parse_dates=["date"])
 
+    no_repeat = False
+    if df["iter"].max() < 1:
+        print("No repeat measurements found.")
+        no_repeat = True
+
     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
+    if not no_repeat:
+        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
+    else:
+        dfm = df0.copy()
+        dfm["dL"] = 0.0
+        dfm["da"] = 0.0
+        dfm["db"] = 0.0
+        dfm["dE"] = 0.0
+        dfm["meanL"] = dfm["L0"]
+        dfm["meana"] = dfm["a0"]
+        dfm["meanb"] = dfm["b0"]
 
     dfm.to_csv(stability_file, index=False, encoding="utf-8")
 
@@ -107,7 +127,7 @@
                 .iloc[0]
                 .to_list()
             )
-            tcc_data.loc[:, "days"] = (tcc_data["date"] - begin_date).dt.days
+            tcc_data.loc[:, "days"] = (tcc_data["date"] - begin_date).dt.total_seconds() // 60
             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]
@@ -142,7 +162,7 @@
     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_xlabel("Minutes")
         ax.set_ylabel("Change in deltaE")
         for paper in paper_list:
             paper_data = df[df["paper"] == paper]
@@ -154,7 +174,7 @@
                     markersize=4,
                     label=f"Paper {paper}",
                 )
-                ax.set_ylim(0, 7)  # Set y-axis limit for better visibility
+                ax.set_ylim(0, 1)  # Set y-axis limit for better visibility
         ax.legend()
     fig.savefig("out_degradation_eachTCC.png", dpi=300, bbox_inches="tight")
 
@@ -173,7 +193,7 @@
                 label=f"Paper {paper} {paper_names[paper]}",
             )
     plt.legend(loc="upper left", borderaxespad=0)
-    plt.xlabel("Days")
+    plt.xlabel("Minutes since first measurement")
     plt.ylabel("Change in deltaE")
     fig2.savefig("out_degradation_meanTCC.png", dpi=300, bbox_inches="tight")