Adding SPC Engine

.gitignore

@@ -1,4 +1,6 @@
 data/
 test.ipynb
 *.csv
-*.xlsx
+*.xlsx
+*.json
+charts/*.png

SPC_System/spc_engine.py

@@ -0,0 +1,132 @@
+import json
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+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})")
+    plt.ylabel(f"Value ({unit})")
+    plt.grid(True)
+    plt.legend()
+
+    plt.tight_layout()
+    # plt.show()
+    chart_path = f"./charts/control_chart_{param}.png"
+    plt.savefig(chart_path, dpi=300, bbox_inches='tight')
+    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()
+
+