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()