import matplotlib.pyplot as plt
import pandas as pd

go = 'y'
parameters = 'slope,intercept,beta0,beta1,Cphi'.split(',')
while go == 'y':
    go = input('continue? ')
    if go != 'y':
        break
    plt.close('all')
    df = pd.read_csv('outputs/iterative_fitter_output_df.csv')
    info_cols = {parameter: [] for parameter in parameters}
    for col in df.columns:
        for parameter in parameters:
            if parameter in col:
                info_cols[parameter].append(col)
    for parameter in parameters:
        mini_df = df[info_cols[parameter]]
        fig, ax = plt.subplots()
        ax.set_title(parameter)
        for col in info_cols[parameter]:
            ax.plot(df['iters'].values,
                    df[col].values,
                    label=col,
                    linestyle='-',
                    marker='o')
        ax.set_xlabel('iteration')
        ax.set_ylabel('Value')
        plt.legend()
        plt.show()
    fig, ax = plt.subplots()
    ax.set_title('best_obj_value')
    ax.plot(df['iters'].values,
            df['best_obj'].values,
            linestyle='-',
            marker='o')
    ax.set_xlabel('iteration')
    ax.set_ylabel('Value')
    plt.show()