import glob

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns

# use this: https://www.estopwatch.net/

def read_file(file_path):
    df = pd.read_csv(file_path)
    df['Elapsed time'] = pd.to_datetime(df['Elapsed time'], errors='coerce')
    return df


def analyze_new_error(run_df, groundtruth_df):
    cumulative_errors = run_df['Elapsed time'] - groundtruth_df['Elapsed time']
    cumulative_errors_in_seconds = cumulative_errors.dt.total_seconds()

    new_errors_in_seconds = cumulative_errors_in_seconds.diff().fillna(cumulative_errors_in_seconds[0])
    new_error_points = new_errors_in_seconds[new_errors_in_seconds != 0].index.tolist()

    return new_errors_in_seconds[new_error_points]

def calculate_statistics(errors):
    if len(errors) == 0:
        return {
            'mean_error': 0,
            'median_error': 0,
            'stddev_error': 0,
            'rmse_error': 0,
            'confidence_interval': (0, 0),
            'error_frequency': 0
        }

    mean_error = np.mean(errors)
    median_error = np.median(errors)
    stddev_error = np.std(errors)
    rmse_error = np.sqrt(np.mean(np.square(errors)))
    
    ci_low, ci_high = stats.t.interval(
        confidence=0.95,
        df=len(errors) - 1,
        loc=mean_error,
        scale=stats.sem(errors) if len(errors) > 1 else 0
    )

    return {
        'mean_error': mean_error,
        'median_error': median_error,
        'stddev_error': stddev_error,
        'rmse_error': rmse_error,
        'confidence_interval': (ci_low, ci_high),
    }


def main():
    groundtruth_file = 'groundtruth.csv'
    run_files = glob.glob('runs/*.csv')

    groundtruth_df = read_file(groundtruth_file)
    run_dfs = {f'run{i+1}': read_file(file) for i, file in enumerate(run_files)}

    total_errors = []
    total_points = 0
    all_errors = []

    for run, df in run_dfs.items():
        errors = analyze_new_error(df, groundtruth_df)
        total_errors.extend(errors)
        all_errors.extend(errors)
        total_points += len(df)
        
        results = calculate_statistics(errors)
        error_frequency = len(errors) / len(df)
        
        print(f"Results for {run}:")
        print(f"Mean New Error: {results['mean_error']:.5f} seconds")
        print(f"Median New Error: {results['median_error']:.5f} seconds")
        print(f"Standard Deviation of New Error: {results['stddev_error']:.5f} seconds")
        print(f"RMSE of New Error: {results['rmse_error']:.5f} seconds")
        print(f"95% Confidence Interval of New Error: ({results['confidence_interval'][0]:.5f}, {results['confidence_interval'][1]:.5f}) seconds")
        print(f"New Error Frequency: {error_frequency*100:.5f} %")
        print('-----------------------------------------')

    total_results = calculate_statistics(total_errors)
    total_error_frequency = len(total_errors) / total_points
    
    print("Total Statistics:")
    print(f"Mean New Error: {total_results['mean_error']:.5f} seconds")
    print(f"Median New Error: {total_results['median_error']:.5f} seconds")
    print(f"Standard Deviation of New Error: {total_results['stddev_error']:.5f} seconds")
    print(f"RMSE of New Error: {total_results['rmse_error']:.5f} seconds")
    print(f"95% Confidence Interval of New Error: ({total_results['confidence_interval'][0]:.5f}, {total_results['confidence_interval'][1]:.5f}) seconds")
    print(f"New Error Frequency: {total_error_frequency*100:.5f} %")
    
    # do plus minus
    print(f"New Error: {total_results['mean_error']:.5f} ± {total_results['confidence_interval'][1] - total_results['mean_error']:.5f} seconds")
    
    plt.figure(figsize=(10, 5))
    sns.histplot(all_errors, bins=12, kde=False)
    plt.title('Distribution of Newly Introduced Errors (macOS)')
    plt.xlabel('Error Duration (seconds)')
    plt.ylabel('Frequency')
    plt.savefig('error_dist', dpi=300)
    plt.show()

if __name__ == "__main__":
    main()