diff --git a/analysis.py b/analysis.py
index 99bc3af..0349e53 100644
--- a/analysis.py
+++ b/analysis.py
@@ -9,10 +9,16 @@
 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",
+    "1A": "Glossy Pro Platinum (open)",
+    "1B": "Glossy Pro Platinum (close)",
+    "2A": "Photo Fine-Grained Luster (open)",
+    "2B": "Photo Fine-Grained Luster (close)",
+    "3A": "Photo Premium Matte (open)",
+    "3B": "Photo Premium Matte (close)",
+    "4A": "Matte Photo (open)",
+    "4B": "Matte Photo (close)",
+    "5A": "ELECOM Matte Photo (open)",
+    "5B": "ELECOM Matte Photo (close)",
 }
 data_dir = r"D:\usr\prog\CM700Reader"
 
@@ -24,15 +30,15 @@
     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"):
+        if not file.startswith("tcc6"):
             continue
         file_path = os.path.join(data_dir, file)
         parts = os.path.splitext(os.path.basename(file))[0].split("_")
-        paper = 2
+        paper = parts[0][5:7]
         datestr = parts[1] + "-" + parts[2]
-        iter = 0 if file[11:12] == "a" else 1
+        # iter = 0 if file[11:12] == "a" else 1
         iter = 0
-        # print(f"Reading {file_path} (Paper: {paper}, Date: {datestr}, Iteration: {iter})")
+        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
@@ -41,6 +47,10 @@
         # 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)
+    if all_data.empty:
+        print("No data found in the specified directory.")
+        exit(1)
+
     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(