import matplotlib.pyplot as plt
import seaborn as sns

def missing_value_pairwise_plots(data_frame, null_column, save=False):
    not_missing_data = data_frame.dropna()
    mising_data = data_frame[data_frame[null_column].isnull()]
    for column in data_frame.columns:
        if column != null_column:
            plt.figure()
            plt.title(f"Scatter Plot of {column} against {null_column}")
            plot_missing_values(column)
            plt.scatter(not_missing_data[column], not_missing_data[null_column], color=[[0.502, 0, 0.502, 0.4]], label="Existing Values")
            plt.xlabel(column)
            plt.ylabel(null_column)
            plt.legend()
            if save:
                plt.savefig(f"missing_values[{column}:{null_column}].png")
            else:
                plt.show()
            plt.close()

def plot_missing_values(column):
    plt.plot([], [], color="red", alpha=0.4, label="Missing Values")
    for value in column:
        plt.axvline(x=value, color="red", alpha=0.4)

def correlation_matrix(data_frame, save=False):
    matrix = data_frame.corr()
    plt.figure()
    sns.heatmap(matrix, annot=True)
    plt.title("Correlation Matrix of Existing Features")
    
    if save:
        plt.savefig("correlation_matrix.png")
    else:
        plt.show()
    plt.close()

def imputation_plots(data_frame, imputed_data, null_column, columns, save=False):
    not_missing_data = data_frame.dropna()
    for column in columns:
        plt.figure()
        plt.scatter(imputed_data[column], imputed_data[null_column], color=[[0, 0.502, 0, 0.4]], label="Imputed Data")
        plt.scatter(not_missing_data[column], not_missing_data[null_column], colot=[[0.502, 0, 0.502, 0,4]], label="Original Data")
        plt.title(f"Scatter Plot of {column} against {null_column} after KNN(9) Imputation")
        plt.xlabel(column)
        plt.ylabel(column)
        plt.legend()
        if save:
            plt.savefig(f"imputation_results[{column}:{null_column}].png")
        else:
            plt.show()
        plt.close()

def outlier_box_plots(data_frame, save=False):
    for column in data_frame.columns:
        plt.figure()
        plt.title(f"Box Plot of {column}")
        plt.boxplot(data_frame[column])
        plt.ylabel(column)
        plt.xticks(rotation=45)
        if save:
            plt.savefig(f"outlier_box_plot[{column}].png")
        else:
            plt.show()
        plt.close()