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import numpy as np
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import pickle
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result=[]
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pad=[-1000]*52
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loacl_gap=10000
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def process_time(timestamp):
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t = timestamp.split()
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t = t[-1].split(":")
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h = float(t[0])
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m = float(t[1])
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t = t[-1].split(".")
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s = float(t[0])
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ms = float(t[1])
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return h * 60 * 60 * 100 + m * 60 * 100 + s * 100 + ms
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with open("./csi_data.pkl", 'rb') as f:
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csi = pickle.load(f)
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for data in csi:
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csi_time=data['csi_time']
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local_time=data['csi_local_time']
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magnitude=data['magnitude']
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phase=data['phase']
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people=data['people']
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last_local=None
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last_glob=None
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current_magnitude=[]
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current_phase=[]
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current_timestamp=[]
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global_timestamp=[]
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for i in range(len(csi_time)):
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if last_local is None:
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last_local=local_time[i]
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last_glob=process_time(csi_time[i])
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current_magnitude.append(magnitude[i])
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current_phase.append(phase[i])
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current_timestamp.append(local_time[i])
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else:
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local = local_time[i]
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glob = process_time(csi_time[i])
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num=round((local-last_local-loacl_gap)/loacl_gap)
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if num>0:
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delta=(local-last_local)/(num+1)
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delta_glob=(glob-last_glob)/(num+1)
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for j in range(num):
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current_magnitude.append(pad)
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current_phase.append(pad)
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current_timestamp.append(current_timestamp[-1] + delta)
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global_timestamp.append(global_timestamp[-1]+delta_glob)
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current_magnitude.append(magnitude[i])
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current_phase.append(phase[i])
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current_timestamp.append(local)
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global_timestamp.append(glob)
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last_local = local
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last_glob = glob
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print(len(current_magnitude))
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result.append({
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'time': np.array(current_timestamp),
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'global_time': np.array(global_timestamp),
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'people': people,
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'magnitude': np.array(current_magnitude),
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'phase': np.array(current_phase)
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})
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output_file = './data_sequence.pkl'
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with open(output_file, 'wb') as f:
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pickle.dump(result, f) |
