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import numpy as np
import pickle
import pandas as pd
result=[]
pad=[-1000]*52
loacl_gap=10000
def process_time(timestamp):
t = timestamp.split()
t = t[-1].split(":")
h = float(t[0])
m = float(t[1])
t = t[-1].split(".")
s = float(t[0])
ms = float(t[1])
return h * 60 * 60 * 100 + m * 60 * 100 + s * 100 + ms
def interpolate_data(original_data):
data_interpolate = np.copy(original_data)
# 对数据进行插值处理
for j in range(original_data.shape[1]):
series = pd.Series(original_data[:, j])
series.interpolate(method='linear', inplace=True)
data_interpolate[:, j] = series.values
return data_interpolate
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=data['people']
last_local=None
last_glob=None
current_magnitude=[]
current_phase=[]
current_timestamp=[]
global_timestamp=[]
for i in range(len(csi_time)):
if last_local is None:
last_local=local_time[i]
last_glob=process_time(csi_time[i])
current_magnitude.append(magnitude[i])
current_phase.append(phase[i])
current_timestamp.append(local_time[i])
else:
local = local_time[i]
glob = process_time(csi_time[i])
num=round((local-last_local-loacl_gap)/loacl_gap)
if num>0:
delta=(local-last_local)/(num+1)
delta_glob=(glob-last_glob)/(num+1)
for j in range(num):
current_magnitude.append(pad)
current_phase.append(pad)
current_timestamp.append(current_timestamp[-1] + delta)
global_timestamp.append(global_timestamp[-1]+delta_glob)
current_magnitude.append(magnitude[i])
current_phase.append(phase[i])
current_timestamp.append(local)
global_timestamp.append(glob)
last_local = local
last_glob = glob
current_magnitude = np.array(current_magnitude)
current_magnitude[current_magnitude == -1000] = np.nan
current_magnitude = interpolate_data(current_magnitude)
print(len(current_magnitude))
result.append({
'time': np.array(current_timestamp),
'global_time': np.array(global_timestamp),
'people': people,
'magnitude': current_magnitude,
'phase': np.array(current_phase)
})
output_file = './data_sequence_linear.pkl'
with open(output_file, 'wb') as f:
pickle.dump(result, f) |