Enhanced SPC Control

.gitignore

@@ -4,3 +4,4 @@ test.ipynb
 *.xlsx
 *.json
 charts/*.png
+charts_compare/*.png

SPC_System/spc_engine.py

@@ -1,145 +1,122 @@
-import json
 import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 import os
 
+# --------- Load three datasets ----------
+df_t1 = pd.read_excel("tester#1.xlsx")
+df_t2 = pd.read_excel("tester#2.xlsx")
+df_gold = pd.read_excel("Golden_Data.xlsx")
+
+# --------- Get parameter list ----------
+df_new = df_gold.head(3).dropna(axis=1, how="all")
+df_param = df_new.drop(["T_TIME", "SITE_NUM"], axis=1)
+parameters = list(df_param.columns)
+
+# --------- Build master dictionary ----------
+data = {
+    param: {
+        "Tester1": pd.to_numeric(df_t1[param].iloc[3:], errors="coerce").dropna().tolist(),
+        "Tester2": pd.to_numeric(df_t2[param].iloc[3:], errors="coerce").dropna().tolist(),
+        "Golden":  pd.to_numeric(df_gold[param].iloc[3:], errors="coerce").dropna().tolist(),
+        "LimitL": df_new[param].iloc[1],
+        "LimitU": df_new[param].iloc[2],
+        "Unit": df_new[param].iloc[0]
+    }
+    for param in parameters
+}
+
+# --------- Plot comparison SPC ----------
+os.makedirs("./charts_compare", exist_ok=True)
+
+for param in parameters:
+
+    t1 = np.array(data[param]["Tester1"])
+    t2 = np.array(data[param]["Tester2"])
+    gold = np.array(data[param]["Golden"])
 
-file = input("Select the file to analyze or type in the file name (Should be .xlsx)")
-INPUT_EXCEL = file
-
-df = pd.read_excel(INPUT_EXCEL)
-
-
-################DICTIONARY BASED ON THE PARAMETER###########################
-import json
-
-df_new = df.head(3)
-##To drop the extra columns
-df_new = df_new.dropna(axis=1, how='all')
-df_param = df_new.drop(["T_TIME","SITE_NUM"],axis=1)
-parameter = list(df_param.columns)
-
-##Convert the parameter to dictionary
-new_dict = {param:{"values":[]} for param in parameter}
-for prm in parameter:
-    temp = dict(zip(df_new['SITE_NUM'],df_new[prm]))
-    new_dict[prm].update(temp)
-
-for prm in parameter:
-    df = df.dropna(axis=1, how='all')
-    __temp = df[prm].values[4:].tolist()
-    new_dict[prm]['values'].extend(__temp)
-
-######To extract the Limits##############
-df_new = df.head(3)
-##To drop the extra columns
-df_new = df_new.dropna(axis=1, how='all')
-df_param = df_new.drop(["T_TIME","SITE_NUM"],axis=1)
-parameter = list(df_param.columns)
-
-##Convert the parameter to dictionary
-new_dict = {param:{"values":[]} for param in parameter}
-for prm in parameter:
-    temp = dict(zip(df_new['SITE_NUM'],df_new[prm]))
-    new_dict[prm].update(temp)
-
-for prm in parameter:
-    df = df.dropna(axis=1, how='all')
-    __temp = df[prm].values[4:].tolist()
-    new_dict[prm]['values'].extend(__temp)
-
-
-#TODO: No need to store json. If in case if we want to maintian history then we can store them 
-# json_data = json.dumps(new_dict)
-# print(json_data)
-
-# with open("output.json", "w") as f:
-#     json.dump(new_dict, f, indent=4)
-
-
-
-#############Plotting###############
-
-data = new_dict
-
-##TODO: To plot for all the paramter. But need to check the way to handle it in the front end. 
-for param in parameter: 
-
-    values = data[param]["values"]
     limit_l = data[param]["LimitL"]
     limit_u = data[param]["LimitU"]
     unit = data[param]["Unit"]
 
-    import numpy as np
-
-    values = np.array(values)
-    mean = np.mean(values)
-    std = np.std(values, ddof=1)  # Sample standard deviation
-
-    UCL = mean + 3*std
-    LCL = mean - 3*std
-
-    plt.figure(figsize=(7, 5))
-
-    # --- Box Plot ---
-    plt.boxplot(values, vert=True, patch_artist=True)
-
-    # --- Jittered Scatter Plot (spread raw values) ---
-    y = values
-    x = np.random.normal(1, 0.04, size=len(values))  # jitter around x=1
-    plt.scatter(x, y, alpha=0.6)
-
-    # --- Reference Lines ---
-    plt.axhline(mean, color='green', linestyle='--', label='Mean')
-    plt.axhline(UCL, color='red', linestyle='-.', label='UCL (Mean + 3σ)')
-    plt.axhline(LCL, color='red', linestyle='--', label='LCL (Mean - 3σ)')
-
-    # Optional spec limits
-    plt.axhline(limit_l, color='orange', linestyle=':', label='Lower Spec Limit')
-    plt.axhline(limit_u, color='orange', linestyle=':', label='Upper Spec Limit')
-
-    # Labels & Styling
-    plt.title(f"Box Plot with All Data Points - {param} ({unit})")
+    # Golden reference control band
+    mean = gold.mean()
+    t1_mean = t1.mean()
+    t2_mean = t2.mean()
+    std = gold.std(ddof=1)
+    UCL = mean + 3 * std
+    LCL = mean - 3 * std
+
+    # ---- Fix max length ----
+    max_len = max(len(t1), len(t2), len(gold))
+    xaxis = np.arange(1, max_len + 1)
+
+    # ---- Correlation ----
+    min_len_t1 = min(len(t1), len(gold))
+    min_len_t2 = min(len(t2), len(gold))
+
+    corr_t1 = np.corrcoef(t1[:min_len_t1], gold[:min_len_t1])[0,1]
+    corr_t2 = np.corrcoef(t2[:min_len_t2], gold[:min_len_t2])[0,1]
+    corr_t1_t2 = np.corrcoef(t1[:min_len_t1], t2[:min_len_t2])[0,1]
+
+    # ---- CPK (based on Golden dataset) ----
+    cpk = min((mean - limit_l) / (3 * std), (limit_u - mean) / (3 * std))
+
+    plt.figure(figsize=(10,6))
+
+    # --- Plot all datasets ---
+    plt.plot(range(1, len(t1)+1), t1, marker='o', label="Tester #1")
+    plt.plot(range(1, len(t2)+1), t2, marker='o', label="Tester #2")
+    plt.plot(range(1, len(gold)+1), gold, marker='o', label="Gold Reference", linewidth=3)
+
+    # --- SPC lines ---
+    plt.axhline(mean, linestyle='--', color='black', label=f"Mean (Gold)")
+    plt.axhline(UCL, linestyle='-.', color='red', label="UCL (Mean + 3σ)")
+    plt.axhline(LCL, linestyle='-.', color='red', label="LCL (Mean – 3σ)")
+
+    # --- Spec Limits ---
+    plt.axhline(limit_l, linestyle=':', color='orange', label="Lower Spec")
+    plt.axhline(limit_u, linestyle=':', color='orange', label="Upper Spec")
+
+    # ---- Fix X-axis ----
+    # plt.xticks(np.arange(1, max_len + 1, step=1))
+    # plt.xlim(1, max_len)
+
+    # ---- Fix X-axis with whole numbers + padding ----
+    max_len = max(len(t1), len(t2), len(gold))
+
+    plt.xticks(np.arange(1, max_len + 1, 1))  # whole numbers
+    plt.xlim(0, max_len + 1)  # space before 1 and after last point
+
+
+    # ---- Add CPK + Correlation text box ----
+    textstr = (
+        f"Gold Mean = {mean:.4f}\n"
+        f"Std Dev = {std:.4f}\n"
+        f"CPK = {cpk:.4f}\n"
+        f"Corr T1–Gold = {corr_t1:.4f}\n"
+        f"Corr T2–Gold = {corr_t2:.4f}\n"
+        f"Corr T1-T2 = {corr_t1_t2:.4f}\n"
+        f"Tester#1 Mean = {t1_mean:.4f}\n"
+        f"Tester#2 Mean = {t2_mean:4f}\n"
+    )
+
+    plt.gca().text(
+        0.02, 0.98, textstr,
+        transform=plt.gca().transAxes,
+        fontsize=10,
+        verticalalignment='top',
+        bbox=dict(boxstyle="round,pad=0.4", facecolor="white", alpha=0.8)
+    )
+
+    # Labels
+    plt.title(f"SPC Comparison - {param} ({unit})")
+    plt.xlabel("Sample Index")
     plt.ylabel(f"Value ({unit})")
     plt.grid(True)
     plt.legend()
-
     plt.tight_layout()
-    # plt.show()
-    # chart_path = f"./charts/control_chart_{param}.png"
 
-    # Folder path
-    folder = "./charts"
-
-    # Create folder if not exists
-    os.makedirs(folder, exist_ok=True)
-
-    # File path
-    chart_path = f"{folder}/control_chart_{param}.png"
-
-
-    plt.savefig(chart_path, dpi=300, bbox_inches='tight')
+    # Save chart
+    plt.savefig(f"./charts_compare/SPC_compare_{param}.png", dpi=300)
     plt.close()
-
-
-    ####line plot
-    # x_axis = range(1, len(values)+1)
-    # plt.figure(figsize=(10,5))
-    # plt.plot(x_axis,values, marker='o', linestyle='-', label='Measurements')
-    # plt.axhline(mean, color='green', linestyle='--', label='Mean')
-    # plt.axhline(UCL, color='red', linestyle='-.', label='UCL (Mean + 3σ)')
-    # plt.axhline(LCL, color='red', linestyle='--', label='LCL (Mean - 3σ)')
-    # plt.axhline(limit_l, color='orange', linestyle=':', label='Lower Spec Limit')
-    # plt.axhline(limit_u, color='orange', linestyle=':', label='Upper Spec Limit')
-
-    # plt.xticks(x_axis)
-    # plt.title(f"SPC Chart - {param} ({unit})")
-    # plt.xlabel("Sample Index")
-    # plt.ylabel(f"Value ({unit})")
-    # plt.legend()
-    # plt.grid(True)
-    # plt.tight_layout()
-    # plt.show()
-
-