diff --git a/src_pyeddl/model_basic_lopo.py b/src_pyeddl/model_basic_lopo.py
index c29d85a..be82d7b 100644
--- a/src_pyeddl/model_basic_lopo.py
+++ b/src_pyeddl/model_basic_lopo.py
@@ -1,101 +1,101 @@
 #Imports
 import numpy as np
 import pandas as pd
 
 # Utility file
 import ModelHandler as mh
 
 # sklearn
 from sklearn.utils import shuffle
 from sklearn.model_selection import train_test_split
 
 
 def main(handler):
     # Msc
     temp_data_folder = "../temp_data/"
     model_file = "../model/model.bin"
-    data_folder = "/home/teijeiro/Documents/databases/chbmit/deephealth/"
+    data_folder = "../dataset/"
     signal_length = "1mn"
 
     # processing
     validation_size=0.15
     test_stride = 640 #Corresponds to 50% overlap, can be set to 1280 to have no overlap
 
     # Model
     epochs = 1
     batch_size = 32
     learning_rate = 0.0075 # 0.00075
 
     with open(data_folder+"signal_mit_"+signal_length+".csv", "rb") as file:
         x_data_ = file.read().splitlines()
 
     y_data = np.loadtxt(data_folder+"labels_mit_"+signal_length+".txt")
     info_data = np.loadtxt(data_folder+"infos_mit_"+signal_length+".txt", dtype="str")
 
     # Convert from string
     x_data = []
     for sig in x_data_:
       x_data.append(np.fromstring(sig, sep=','))
 
     # Reshape in 2D as data are 1D in csv file
     x_data = [np.reshape(np.array(x_data_i), (handler.NB_CHNS, int(len(x_data_i)/handler.NB_CHNS))) for x_data_i in x_data]
 
     # Create the pandas df
     data = pd.concat([pd.Series(y_data), pd.Series([info[0] for info in info_data]),pd.Series([info[1] for info in info_data])], axis=1)
     data.columns = ["label", "patient", "file"]
     data["signal"] = ""
     data["signal"].astype(object)
     for i, sig in enumerate(x_data):
         data.at[i, "signal"] = sig
 
     patients = np.unique(data.patient)
     data.sort_values(["patient", "file", "label"], inplace=True)
     data = data.reset_index(drop=True)
 
     # Load seizure times
     seizures = pd.read_csv(data_folder+"seizures.csv", delimiter='\t')
     seizures_ = seizures[seizures.Patient.isin(patients)]
     seizures_["length"] = seizures_.apply(lambda x: (x.end_seizure - x.start_seizure), axis=1)
 
     results = {}
 
     for patient in patients:
         print("Patient: ", patient)
         patient_data = data[data.patient == patient]
         files = np.unique(patient_data.file)
         print('   ', len(files), ' files.')
 
         test_data = patient_data
         train_data = data.drop(patient_data.index)
 
         #Build train/test set by cutting each signals in pieces of 5 seconds, with 50% overlap
         x_train, y_train, x_test, y_test = handler.cut_signal_data(train_data, test_data, stride_test=test_stride)
 
         #Shuffle and balance classes
         x_train, y_train = shuffle(x_train, y_train)
         x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=validation_size, stratify = y_train)
         x_train = np.array(x_train)
         x_val = np.array(x_val)
         x_train = x_train.reshape((len(x_train), handler.NB_CHNS, 1280, 1))
         x_val = x_val.reshape((len(x_val), handler.NB_CHNS, 1280, 1))
         y_train = np.eye(2)[np.array(y_train).astype(int)]
         y_val = np.eye(2)[np.array(y_val).astype(int)]
 
         handler.train(model_file, epochs, batch_size, learning_rate, x_train, y_train, x_val, y_val)
 
         x_test = np.array(x_test)
 
         x_test_splitted = np.array_split(x_test, len(files))
         y_test_splitted = np.array_split(np.array(y_test), len(files))
 
         for x_test, y_test, file in zip(x_test_splitted, y_test_splitted, files):
             x_test = np.array(x_test)
             x_test = x_test.reshape((len(x_test), handler.NB_CHNS, 1280, 1))
             y_test = np.eye(2)[np.array(y_test).astype(int)]
 
             handler.evaluate(model_file, x_test, y_test)
 
 
 if __name__ == '__main__':
     model_handler = mh.ModelHandler()
     main(model_handler)