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import pickle
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import numpy as np
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with open("./data_sequence.pkl", 'rb') as f:
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data_csi = pickle.load(f)
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with open("./gt_data.pkl", 'rb') as f:
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data_cv = pickle.load(f)
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data = []
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pad = -1000
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for k in range(len(data_csi)):
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csi = data_csi[k]
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cv = data_cv[k]
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x = cv['x']
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y = cv['y']
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img_path = np.array(cv['img_path'])
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time_cv = cv['timestamp']
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print(cv['people_name'])
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indices = np.argsort(time_cv)
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x = x[indices]
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y = y[indices]
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img_path = img_path[indices]
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time_cv = time_cv[indices]
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print(x)
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print(y)
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csi_time = csi['global_time']
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local_time = csi['time']
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magnitude = csi['magnitude']
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phase = csi['phase']
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people = csi['people']
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indices = np.argsort(csi_time)
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local_time = local_time[indices]
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magnitude = magnitude[indices]
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csi_time = csi_time[indices]
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phase = phase[indices]
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x_list = []
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y_list = []
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path_list = []
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i = 0
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j = 0
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print(csi_time)
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print(time_cv)
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while csi_time[i] < time_cv[j]:
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i += 1
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x_list.append(pad)
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y_list.append(pad)
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path_list.append(pad)
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while i < len(csi_time):
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while csi_time[i] > time_cv[j]:
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j += 1
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if j >= len(time_cv):
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break
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if j >= len(time_cv):
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break
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x_list.append(x[j])
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y_list.append(y[j])
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path_list.append(img_path[j])
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i += 1
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print(len(x_list))
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if len(x_list) < len(csi_time):
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num = len(csi_time) - len(x_list)
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x_list = x_list + [pad] * num
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y_list = y_list + [pad] * num
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path_list = path_list + [pad] * num
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data.append({
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'magnitude': magnitude,
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'phase': phase,
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'x': x_list,
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'y': y_list,
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'img_path': path_list,
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'time': local_time,
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'people': people
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})
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print(people)
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print(len(magnitude),len(phase),len(x_list),len(y_list),len(path_list),len(local_time))
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output_file = './wiloc.pkl'
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with open(output_file, 'wb') as f:
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pickle.dump(data, f)
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