| import numpy as np
|
| import pickle
|
|
|
|
|
| result=[]
|
|
|
| pad=[-1000]*52
|
| loacl_gap=10000
|
|
|
|
|
| with open("./csi_data.pkl", 'rb') as f:
|
| csi = pickle.load(f)
|
|
|
| for data in csi:
|
| csi_time=data['csi_time']
|
| local_time=data['csi_local_time']
|
| magnitude=data['magnitude']
|
| phase=data['phase']
|
| people_num=data['people_num']
|
|
|
| last_local=None
|
| current_magnitude=[]
|
| current_phase=[]
|
| current_timestamp=[]
|
| for i in range(len(csi_time)):
|
| if last_local is None:
|
| last_local=local_time[i]
|
| current_magnitude.append(magnitude[i])
|
| current_phase.append(phase[i])
|
| current_timestamp.append(local_time[i])
|
| else:
|
| local = local_time[i]
|
| num=round((local-last_local-loacl_gap)/loacl_gap)
|
| if num>0:
|
| delta=(local-last_local)/(num+1)
|
| for j in range(num):
|
| current_magnitude.append(pad)
|
| current_phase.append(pad)
|
| current_timestamp.append(current_timestamp[-1] + delta)
|
| current_magnitude.append(magnitude[i])
|
| current_phase.append(phase[i])
|
| current_timestamp.append(local_time[i])
|
| last_local=local
|
|
|
| print(len(current_magnitude))
|
| result.append({
|
| 'time': np.array(current_timestamp),
|
| 'action': people_num,
|
| 'people': people_num,
|
| 'magnitude': np.array(current_magnitude),
|
| 'phase': np.array(current_phase)
|
| })
|
|
|
| output_file = './data_sequence.pkl'
|
| with open(output_file, 'wb') as f:
|
| pickle.dump(result, f)
|
|
|
|
|
