import pandas as pd
import numpy as np
#
#
#
# def load_data(file_path):
#     return pd.read_csv(file_path)
#
#
# def preprocess_data(data):
#     # 按指定列分组
#     grouped = data.groupby(['ParticipantID', 'BlockID', 'TrialID'])
#
#     # 计算每个组的均值和标准差，再计算尺寸的上限
#     group_sizes = grouped.size()
#     upper_limit = group_sizes.mean() + 1 * group_sizes.std()
#
#     # 使用filter方法保留组大小小于上限的数据
#     filtered_data = grouped.filter(lambda x: len(x) < upper_limit)
#     return filtered_data
#
#
# def save_data(data, path):
#     # 确保目标文件夹存在
#     os.makedirs(os.path.dirname(path), exist_ok=True)
#     # 保存数据
#     data.to_csv(path, index=False)
#
#
# # 主函数
# if __name__ == "__main__":
#     for i in range(79, 80):
#         output_train_path = f'../Data/Study2Evaluation/Preprocessed/{i}_train_data_preprocessed_evaluation.csv'
#         output_test_path = f'../Data/Study2Evaluation/Preprocessed/{i}_test_data_preprocessed_evaluation.csv'
#
#         data1 = load_data(output_train_path)
#         data2 = load_data(output_test_path)
#
#         cleaned_data1 = preprocess_data(data1)
#         cleaned_data2 = preprocess_data(data2)
#
#         # 定义保存路径
#         save_path_train = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_train_data_preprocessed_evaluation.csv'
#         save_path_test = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_test_data_preprocessed_evaluation.csv'
#
#         # 保存清洗后的数据
#         save_data(cleaned_data1, save_path_train)
#         save_data(cleaned_data2, save_path_test)
#
#         print(f"Cleaned data saved for {i} train and test.")

def load_data(file_path):
    return pd.read_csv(file_path)

# 数据预处理，包括特征选择、后填充和展平
def preprocess_data(data):
    grouped = data.groupby(['ParticipantID', 'BlockID', 'TrialID'])
    group_sizes = grouped.size()
    max_group_index = group_sizes.idxmax()
    max_group_rows = grouped.get_group(max_group_index).shape[0]
    return max_group_rows

if __name__ == "__main__":
    max_rows=0
    for i in range(79, 80):
        # if i ==3 or i ==6 or i ==15 or i ==19 or i== 22:
        #     continue
        output_train_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_train_data_preprocessed_evaluation.csv'
        output_test_path = f'../Data/Study2Evaluation/Preprocessed/cleaned/{i}_test_data_preprocessed_evaluation.csv'
        data1=load_data(output_train_path)
        data2=load_data(output_test_path)
        max_group_rows1 = preprocess_data(data1)
        max_group_rows2 = preprocess_data(data2)
        max_rows=max(max_group_rows1,max_group_rows2)
        print(max_rows)