--- a
+++ b/WESAD/WESAD_Data_Numpy.py
@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+from sklearn.utils import shuffle
+
+df = pd.read_csv("../allchest.csv")
+df_list = list(map(lambda x: df[df['ID'] == x], [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17]))
+df_list = list(map(lambda x: x[x['label'] <= 4], df_list))
+del df
+
+train = []
+test = []
+
+for df in df_list:
+	df.reset_index(inplace=True, drop=True)
+	df = shuffle(df, random_state=42)
+	df.reset_index(inplace=True, drop=True)
+	train.append(df.iloc[:int(0.6 * df.shape[0]), :])
+	test.append(df.iloc[int(0.6 * df.shape[0]):, :])
+del df_list
+
+train = pd.concat(train, axis=0)
+test = pd.concat(test, axis=0)
+train.reset_index(inplace=True, drop=True)
+test.reset_index(inplace=True, drop=True)
+train = train[['label', 'ID', 'chestACCx', 'chestACCy', 'chestACCz', 'chestECG', 'chestEMG', 'chestEDA', 'chestTemp', 'chestResp']]
+test = test[['label', 'ID', 'chestACCx', 'chestACCy', 'chestACCz', 'chestECG', 'chestEMG', 'chestEDA', 'chestTemp', 'chestResp']]
+
+train = np.array(train, dtype=np.float32)
+test = np.array(test, dtype=np.float32)
+
+np.save("train.npy", train)
+np.save("test.npy", test)