| import numpy as np
|
| import pickle
|
| import os
|
| import pandas as pd
|
|
|
| root="./data"
|
| data=[]
|
| csi_vaid_subcarrier_index = range(0, 52)
|
|
|
| def handle_complex_data(x, valid_indices):
|
| real_parts = []
|
| imag_parts = []
|
| for i in valid_indices:
|
| real_parts.append(x[i * 2])
|
| imag_parts.append(x[i * 2 - 1])
|
| return np.array(real_parts) + 1j * np.array(imag_parts)
|
|
|
|
|
|
|
| people_id=0
|
| for people in os.listdir(root):
|
| print(people)
|
| action_id=0
|
| path_people=os.path.join(root,people)
|
| for action in os.listdir(path_people):
|
| count=0
|
| print(action)
|
| path_action=os.path.join(path_people,action)
|
| for file in os.listdir(path_action):
|
| count+=1
|
| path=os.path.join(path_action,file)
|
| df = pd.read_csv(path)
|
| df.dropna(inplace=True)
|
| df['data'] = df['data'].apply(lambda x: eval(x))
|
| complex_data = df['data'].apply(lambda x: handle_complex_data(x, csi_vaid_subcarrier_index))
|
| magnitude = complex_data.apply(lambda x: np.abs(x))
|
| phase = complex_data.apply(lambda x: np.angle(x, deg=True))
|
| time = np.array(df['timestamp'])
|
| local_time = np.array(df['local_timestamp'])
|
| data.append({
|
| 'csi_time':time,
|
| 'csi_local_time':local_time,
|
| 'volunteer_name': people,
|
| 'volunteer_id': people_id,
|
| 'action': action,
|
| 'action_id': action_id,
|
| 'magnitude': np.array([np.array(a) for a in magnitude]),
|
| 'phase': np.array([np.array(a) for a in phase])
|
| })
|
|
|
|
|
| action_id+=1
|
| people_id+=1
|
|
|
|
|
| output_file = './csi_data.pkl'
|
| with open(output_file, 'wb') as f:
|
| pickle.dump(data, f) |
