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import numpy as np
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import pickle
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import os
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import pandas as pd
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root="./data/"
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data=[]
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csi_vaid_subcarrier_index = range(0, 52)
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def handle_complex_data(x, valid_indices):
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real_parts = []
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imag_parts = []
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for i in valid_indices:
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real_parts.append(x[i * 2])
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imag_parts.append(x[i * 2 - 1])
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return np.array(real_parts) + 1j * np.array(imag_parts)
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for people_num in os.listdir(root):
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if len(people_num)>1:
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continue
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print(people_num)
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path=os.path.join(root,people_num)
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for file in os.listdir(path):
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if file[-3:] != "csv":
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continue
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print(file)
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df = pd.read_csv(os.path.join(path,file))
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df.dropna(inplace=True)
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df['data'] = df['data'].apply(lambda x: eval(x))
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complex_data = df['data'].apply(lambda x: handle_complex_data(x, csi_vaid_subcarrier_index))
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magnitude = complex_data.apply(lambda x: np.abs(x))
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phase = complex_data.apply(lambda x: np.angle(x, deg=True))
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time = np.array(df['timestamp'])
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local_time = np.array(df['local_timestamp'])
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data.append({
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'csi_time':time,
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'csi_local_time':local_time,
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'people_num': eval(people_num),
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'magnitude': np.array([np.array(a) for a in magnitude]),
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'phase': np.array([np.array(a) for a in phase]),
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'CSI': np.array([np.array(a) for a in complex_data])
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})
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output_file = './csi_data.pkl'
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with open(output_file, 'wb') as f:
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pickle.dump(data, f) |
