import pandas as pd import argparse import os import numpy as np from scipy.ndimage import gaussian_filter1d from sklearn.preprocessing import MinMaxScaler #这个文件是用来在原始文件的基础上删除一些Trial，进行特征工程，然后再打上标签 def angle_between_vectors(v1, v2): """Calculate the angle between two vectors.""" v1 = v1 / np.linalg.norm(v1) v2 = v2 / np.linalg.norm(v2) u_minus_v = v1 - v2 u_plus_v = v1 + v2 angle = 2 * np.arctan2(np.linalg.norm(u_minus_v), np.linalg.norm(u_plus_v)) return np.degrees(angle) def smooth_velocity(velocity, sigma=5): return gaussian_filter1d(velocity, sigma=sigma) def calculate_angular_velocity(forward_vectors, timestamps): """计算角速度""" angles = np.array( [angle_between_vectors(forward_vectors[i], forward_vectors[i + 1]) if i + 1 < len(forward_vectors) else 0 for i in range(len(forward_vectors) - 1)]) # 注意这里的变化，排除了最后一个向量的计算 # 计算时间间隔 times = np.diff(timestamps)/1000 # 计算角速度，对于第一个时间点，将角度设置为0 if len(times) > 0: # 检查times是否非空，避免除以空数组 angular_velocities = np.insert(angles / times, 0, 0) # 在结果数组的开始插入0 else: angular_velocities = np.array([0]) # 如果times为空，说明只有一个时间点，无法计算速度 return angular_velocities def calculate_linear_velocity(positions, timestamps): """计算线性速度""" distances = np.linalg.norm(np.diff(positions, axis=0), axis=1) times = np.diff(timestamps)/1000 linear_velocities = np.insert(distances / times, 0,0) # Insert 0 at the start as there's no velocity at the first timestamp return linear_velocities def calculate_direction(start_position, current_position): """计算方向""" direction = current_position - start_position norm = np.linalg.norm(direction) return direction / norm if norm != 0 else np.zeros_like(direction) def calculate_acceleration(velocities, timestamps): """根据速度计算加速度""" accels = [0] # 第一个时间点的加速度假设为0 for i in range(1, len(velocities)): accel = (velocities[i] - velocities[i-1]) / ((timestamps[i] - timestamps[i-1])/1000) accels.append(accel) return np.array(accels) ## 特征工程，先加上三个模态的角速度，和旋转角，再加上手的线性速度和移动距离 def process_single_sequence(df): """处理groupBy之后的单个序列DataFrame，计算所有模态的角速度、线性速度、方向和旋转轴""" # 提取时间戳 timestamps = df['TimeStamp'].values # 定义所有需要计算的模态 modalities = ['HMD', 'Hand', 'Leye'] for modality in modalities: # 计算角速度和旋转轴 if all(f'{modality}ForwardV{i}' in df.columns for i in ['X', 'Y', 'Z']): forward_vectors = df[[f'{modality}ForwardVX', f'{modality}ForwardVY', f'{modality}ForwardVZ']].values initial_forward_vector = forward_vectors[0] df[f'{modality}A'] = [(angle_between_vectors(initial_forward_vector, fv)) for fv in forward_vectors] angular_velocity = calculate_angular_velocity(forward_vectors, timestamps) smoothed_angular_velocity = smooth_velocity(angular_velocity) df[f'{modality}AV'] = smoothed_angular_velocity df[f'{modality}AAcc'] = calculate_acceleration(smoothed_angular_velocity, timestamps) # 计算旋转轴并进行标准化以仅保留方向信息 if modality == 'Hand': initial_forward_vector = forward_vectors[0] rotation_axes = np.array([np.cross(initial_forward_vector, fv) for fv in forward_vectors]) rotation_axes_normalized = np.array( [axis / np.linalg.norm(axis) if np.linalg.norm(axis) != 0 else np.array([0.0, 0.0, 0.0]) for axis in rotation_axes] ) df[f'{modality}RotationAxis_X'] = rotation_axes_normalized[:, 0] df[f'{modality}RotationAxis_Y'] = rotation_axes_normalized[:, 1] df[f'{modality}RotationAxis_Z'] = rotation_axes_normalized[:, 2] # 对于HMD和Hand，还需计算线性速度和方向还有移动距离 if modality in ['HMD', 'Hand'] and all(f'{modality}Position{i}' in df.columns for i in ['X', 'Y', 'Z']): positions = df[[f'{modality}PositionX', f'{modality}PositionY', f'{modality}PositionZ']].values initial_position = positions[0] linear_velocity = calculate_linear_velocity(positions, timestamps) df[f'{modality}L'] = [np.linalg.norm(pos-initial_position) for pos in positions] smoothed_velocity = smooth_velocity(linear_velocity) df[f'{modality}LV'] = smoothed_velocity df[f'{modality}LAcc'] = calculate_acceleration(smoothed_velocity, timestamps) # 新特性：加速度 # 计算方向 if modality == 'Hand': start_position = positions[0] directions = np.array([calculate_direction(start_position, pos) for pos in positions]) df[f'{modality}Direction_X'] = directions[:, 0] df[f'{modality}Direction_Y'] = directions[:, 1] df[f'{modality}Direction_Z'] = directions[:, 2] return df def label_trials_with_motion_metrics(df): # Define a helper function for labeling each group def label_group(group): # For the 'HandLinearDistance' and 'HandAngularDistance', take the last value in the group as it represents the total total_linear_distance = group['HandL'].iloc[-1] total_angular_distance = group['HandA'].iloc[-1] # Assign these totals to new columns for every row in the group group['LLabel'] = total_linear_distance group['ALabel'] = total_angular_distance return group # Apply the labeling function to each trial group df_labeled = df.groupby(['ParticipantID', 'BlockID', 'TrialID'], group_keys=False).apply(label_group) return df_labeled def split_data_by_theta_grouped(df): grouped = df.groupby(['ParticipantID', 'BlockID', 'TrialID']) theta_groups = {} # 将每个组添加到对应Theta值的列表中 for _, group in grouped: theta_value = group['Theta'].iloc[0] if theta_value not in theta_groups: theta_groups[theta_value] = [] theta_groups[theta_value].append(group) train_dfs = [] test_dfs = [] for theta_value, groups in theta_groups.items(): # 计算训练集大小 n_train = int(len(groups) * 0.8) # 随机选择训练集序列 np.random.seed(1) # 确保可重复性 train_indices = np.random.choice(len(groups), size=n_train, replace=False) train_groups = [groups[i] for i in train_indices] # 选择测试集序列 test_groups = [groups[i] for i in range(len(groups)) if i not in train_indices] # 将训练集和测试集序列合并 train_df = pd.concat(train_groups) test_df = pd.concat(test_groups) train_dfs.append(train_df) test_dfs.append(test_df) # 合并所有训练集和测试集的DataFrame final_train_df = pd.concat(train_dfs).sort_index() final_test_df = pd.concat(test_dfs).sort_index() return final_train_df, final_test_df def split_data_by_theta_grouped(df): grouped = df.groupby(['ParticipantID', 'BlockID', 'TrialID']) theta_groups = {} # 将每个组添加到对应Theta值的列表中 for _, group in grouped: theta_value = group['Theta'].iloc[0] if theta_value not in theta_groups: theta_groups[theta_value] = [] theta_groups[theta_value].append(group) train_dfs = [] test_dfs = [] for theta_value, groups in theta_groups.items(): # 计算训练集大小 n_train = int(len(groups) * 0.8) # 随机选择训练集序列 np.random.seed(1) # 确保可重复性 train_indices = np.random.choice(len(groups), size=n_train, replace=False) train_groups = [groups[i] for i in train_indices] # 选择测试集序列 test_groups = [groups[i] for i in range(len(groups)) if i not in train_indices] # 将训练集和测试集序列合并 train_df = pd.concat(train_groups) test_df = pd.concat(test_groups) train_dfs.append(train_df) test_dfs.append(test_df) # 合并所有训练集和测试集的DataFrame final_train_df = pd.concat(train_dfs).sort_index() final_test_df = pd.concat(test_dfs).sort_index() return final_train_df, final_test_df # 定义一个函数，将特征缩放到指定的范围内 def preprocess_features(train_df, test_df): # 定义包含负值的特征列和其他特征列 negative_value_features = ['HandAAcc', 'HMDAAcc',"LeyeAAcc","HandLAcc","HMDLAcc"] other_features = ["HMDA", "HMDAV", "HandA", "HandAV", "LeyeA", "LeyeAV","HMDL", "HMDLV","HandL", "HandLV"] # 为包含负值的特征和其他特征创建两个不同的缩放器 scaler_negatives = MinMaxScaler(feature_range=(-1, 1)) scaler_others = MinMaxScaler(feature_range=(0, 1)) # 对训练集应用fit_transform，对测试集应用transform train_df[negative_value_features] = scaler_negatives.fit_transform(train_df[negative_value_features]) test_df[negative_value_features] = scaler_negatives.transform(test_df[negative_value_features]) train_df[other_features] = scaler_others.fit_transform(train_df[other_features]) test_df[other_features] = scaler_others.transform(test_df[other_features]) return train_df, test_df #这个function用于修改Evaluation的数据集 def main(Participant_ID): # 读入数据 input_file_path = f'../Data/Study2Evaluation/{Participant_ID}_Trajectory.csv' output_train_path = f'../Data/Study2Evaluation/Preprocessed/{Participant_ID}_train_data_preprocessed_evaluation.csv' output_test_path = f'../Data/Study2Evaluation/Preprocessed/{Participant_ID}_test_data_preprocessed_evaluation.csv' data=pd.read_csv(input_file_path) data_cleaned = data.loc[:, ~data.columns.str.contains('^Unnamed')] data_no_error = data_cleaned[data_cleaned['isError'] == False] final_data = data_no_error[(data_no_error['TrialID'] != 0) & (data_no_error['TrialID'] != 6) & (data_no_error['TrialID'] != 12) & (data_no_error['TrialID'] != 18) & (data_no_error['TrialID'] != 24) & (data_no_error['TrialID'] != 30) & (data_no_error['TrialID'] != 36)] # 特征工程 processed_groups = final_data.groupby(['ParticipantID','BlockID', 'TrialID']).apply(process_single_sequence) processed_df = processed_groups.reset_index(drop=True) # 为处理完的数据添加标签 df_with_features_labelled = label_trials_with_motion_metrics(processed_df.copy()) final_train_df, final_test_df = split_data_by_theta_grouped(df_with_features_labelled) # 特征预处理 final_train_df_preprocessed, final_test_df_preprocessed = preprocess_features(final_train_df.copy(), final_test_df.copy()) # 保存处理后的数据集到CSV文件 final_train_df_preprocessed.to_csv(output_train_path, index=False) final_test_df_preprocessed.to_csv(output_test_path, index=False) # def main(Participant_ID): # # 读入数据 # input_file_path = f'../Data/{Participant_ID}_Trajectory.csv' # output_train_path = f'../Data/Study1AllUSers/Preprocessed/{Participant_ID}_train_data_preprocessed.csv' # output_test_path = f'../Data/Study1AllUSers/Preprocessed/{Participant_ID}_test_data_preprocessed.csv' # data = pd. read_csv(input_file_path) # participant_id = int(os.path.basename(input_file_path).split('_')[0]) # data.insert(0, 'ParticipantID', participant_id) # data_cleaned = data.loc[:, ~data.columns.str.contains('^Unnamed')] # data_no_error = data_cleaned[data_cleaned['isError'] == False] # cleaned_data_path = '../Data/cleaned_data_trimmed.xlsx' # cleaned_data = pd.read_excel(cleaned_data_path) # filtered_data = pd.merge(data_no_error, cleaned_data, on=['ParticipantID', 'BlockID', 'TrialID'], how='inner') # final_data = filtered_data[(filtered_data['TrialID'] != 0) & (filtered_data['TrialID'] != 8) & (filtered_data['TrialID'] != 16) & (filtered_data['TrialID'] != 24)] # # 特征工程 # processed_groups = final_data.groupby(['ParticipantID','BlockID', 'TrialID']).apply(process_single_sequence) # processed_df = processed_groups.reset_index(drop=True) # # 为处理完的数据添加标签 # df_with_features_labelled = label_trials_with_motion_metrics(processed_df.copy()) # final_train_df, final_test_df = split_data_by_theta_grouped(df_with_features_labelled) # # 特征预处理 # final_train_df_preprocessed, final_test_df_preprocessed = preprocess_features(final_train_df.copy(), final_test_df.copy()) # # 保存处理后的数据集到CSV文件 # final_train_df_preprocessed.to_csv(output_train_path, index=False) # final_test_df_preprocessed.to_csv(output_test_path, index=False) if __name__ == '__main__': for i in range(79, 80): main(str(i))