--- a
+++ b/read_data.py
@@ -0,0 +1,248 @@
+import os
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+import neurokit as nk
+import seaborn as sns
+import pandas as pd
+
+def load_data(path, subject):
+    """Given path and subject, load the data of the subject"""
+    os.chdir(path)
+    os.chdir(subject)
+    with open(subject + '.pkl', 'rb') as file:
+        data = pickle.load(file, encoding='latin1')
+    return data
+
+class read_data_one_subject:
+    """Read data from WESAD dataset"""
+    def __init__(self, path, subject):
+        self.keys = ['label', 'subject', 'signal']
+        self.signal_keys = ['wrist', 'chest']
+        self.chest_sensor_keys = ['ACC', 'ECG', 'EDA', 'EMG', 'Resp', 'Temp']
+        self.wrist_sensor_keys = ['ACC', 'BVP', 'EDA', 'TEMP']
+        os.chdir(path)
+        os.chdir(subject)
+        with open(subject + '.pkl', 'rb') as file:
+            data = pickle.load(file, encoding='latin1')
+        self.data = data
+
+    def get_labels(self):
+        return self.data[self.keys[0]]
+
+    def get_wrist_data(self):
+        """"""
+        #label = self.data[self.keys[0]]
+        #assert subject == self.data[self.keys[1]]
+        signal = self.data[self.keys[2]]
+        wrist_data = signal[self.signal_keys[0]]
+        #wrist_ACC = wrist_data[self.wrist_sensor_keys[0]]
+        #wrist_ECG = wrist_data[self.wrist_sensor_keys[1]]
+        return wrist_data
+
+    def get_chest_data(self):
+        """"""
+        signal = self.data[self.keys[2]]
+        chest_data = signal[self.signal_keys[1]]
+        return chest_data
+
+def extract_mean_std_features(ecg_data, label=0, block = 700):
+    #print (len(ecg_data))
+    i = 0
+    mean_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
+    std_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
+    max_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
+    min_features = np.empty(int(len(ecg_data)/block), dtype=np.float64)
+
+    idx = 0
+    while i < len(ecg_data):
+        temp = ecg_data[i:i+block]
+        #print(len(temp))
+        if idx < int(len(ecg_data)/block):
+            mean_features[idx] = np.mean(temp)
+            std_features[idx] = np.std(temp)
+            min_features[idx] = np.amin(temp)
+            max_features[idx] = np.amax(temp)
+        i += 700
+        idx += 1
+    #print(len(mean_features), len(std_features))
+    #print(mean_features, std_features)
+    features = {'mean':mean_features, 'std':std_features, 'min':min_features, 'max':max_features}
+
+    one_set = np.column_stack((mean_features, std_features, min_features, max_features))
+    return one_set
+
+def extract_one(chest_data_dict, idx, l_condition=0):
+    ecg_data = chest_data_dict["ECG"][idx].flatten()
+    ecg_features = extract_mean_std_features(ecg_data, label=l_condition)
+    #print(ecg_features.shape)
+
+    eda_data = chest_data_dict["EDA"][idx].flatten()
+    eda_features = extract_mean_std_features(eda_data, label=l_condition)
+    #print(eda_features.shape)
+
+    emg_data = chest_data_dict["EMG"][idx].flatten()
+    emg_features = extract_mean_std_features(emg_data, label=l_condition)
+    #print(emg_features.shape)
+
+    temp_data = chest_data_dict["Temp"][idx].flatten()
+    temp_features = extract_mean_std_features(temp_data, label=l_condition)
+    #print(temp_features.shape)
+
+    baseline_data = np.hstack((eda_features, temp_features, ecg_features, emg_features))
+    #print(len(baseline_data))
+    label_array = np.full(len(baseline_data), l_condition)
+    #print(label_array.shape)
+    #print(baseline_data.shape)
+    baseline_data = np.column_stack((baseline_data, label_array))
+    #print(baseline_data.shape)
+    return baseline_data
+
+def recur_print(ecg):
+    while ecg is dict:
+        print(ecg.keys())
+        for k in ecg.keys():
+            recur_print(ecg[k])
+
+def execute():
+    data_set_path = "/media/jac/New Volume/Datasets/WESAD"
+    file_path = "ecg.txt"
+    subject = 'S3'
+    obj_data = {}
+    labels = {}
+    all_data = {}
+    subs = [2, 3, 4, 5, 6]
+    for i in subs:
+        subject = 'S' + str(i)
+        print("Reading data", subject)
+        obj_data[subject] = read_data_one_subject(data_set_path, subject)
+        labels[subject] = obj_data[subject].get_labels()
+
+        wrist_data_dict = obj_data[subject].get_wrist_data()
+        wrist_dict_length = {key: len(value) for key, value in wrist_data_dict.items()}
+
+        chest_data_dict = obj_data[subject].get_chest_data()
+        chest_dict_length = {key: len(value) for key, value in chest_data_dict.items()}
+        print(chest_dict_length)
+        chest_data = np.concatenate((chest_data_dict['ACC'], chest_data_dict['ECG'], chest_data_dict['EDA'],
+                                     chest_data_dict['EMG'], chest_data_dict['Resp'], chest_data_dict['Temp']), axis=1)
+        # Get labels
+
+
+        # 'ACC' : 3, 'ECG' 1: , 'EDA' : 1, 'EMG': 1, 'RESP': 1, 'Temp': 1  ===> Total dimensions : 8
+        # No. of Labels ==> 8 ; 0 = not defined / transient, 1 = baseline, 2 = stress, 3 = amusement,
+        # 4 = meditation, 5/6/7 = should be ignored in this dataset
+
+        # Do for each subject
+        baseline = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 1])
+        # print("Baseline:", chest_data_dict['ECG'][baseline].shape)
+        # print(baseline.shape)
+
+        stress = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 2])
+        # print(stress.shape)
+
+        amusement = np.asarray([idx for idx, val in enumerate(labels[subject]) if val == 3])
+        # print(amusement.shape)
+
+        baseline_data = extract_one(chest_data_dict, baseline, l_condition=1)
+        stress_data = extract_one(chest_data_dict, stress, l_condition=2)
+        amusement_data = extract_one(chest_data_dict, amusement, l_condition=3)
+
+        full_data = np.vstack((baseline_data, stress_data, amusement_data))
+        print("One subject data", full_data.shape)
+        all_data[subject] = full_data
+
+    i = 0
+    for k, v in all_data.items():
+        if i == 0:
+            data = all_data[k]
+            i += 1
+        print(all_data[k].shape)
+        data = np.vstack((data, all_data[k]))
+
+    print(data.shape)
+    return data
+
+if __name__ == '__main__':
+    execute()
+    """
+    ecg, eda = chest_data_dict['ECG'], chest_data_dict['EDA']
+    x = [i for i in range(len(baseline))]
+    for one in baseline:
+        x = [i for i in range(99)]
+        plt.plot(x, ecg[one:100])
+        break
+    """
+    #x = [i for i in range(10000)]
+    #plt.plot(x, chest_data_dict['ECG'][:10000])
+    #plt.show()
+
+    # BASELINE
+
+    #                                    [ecg_features[k] for k in ecg_features.keys()])
+
+    #ecg = nk.ecg_process(ecg=ecg_data, rsp=chest_data_dict['Resp'][baseline].flatten(), sampling_rate=700)
+    #print(os.getcwd())
+
+    """
+    #recur_print
+    print(type(ecg))
+    print(ecg.keys())
+    for k in ecg.keys():
+        print(k)
+        for i in ecg[k].keys():
+            print(i)
+    
+
+    resp = nk.eda_process(eda=chest_data_dict['EDA'][baseline].flatten(), sampling_rate=700)
+    resp = nk.rsp_process(chest_data_dict['Resp'][baseline].flatten(), sampling_rate=700)
+    for k in resp.keys():
+        print(k)
+        for i in resp[k].keys():
+            print(i)
+    
+    # For baseline, compute mean, std, for each 700 samples. (1 second values)
+
+    #file_path = os.getcwd()
+    with open(file_path, "w") as file:
+        #file.write(str(ecg['df']))
+        file.write(str(ecg['ECG']['HRV']['RR_Intervals']))
+        file.write("...")
+        file.write(str(ecg['RSP']))
+        #file.write("RESP................")
+        #file.write(str(resp['RSP']))
+        #file.write(str(resp['df']))
+        #print(type(ecg['ECG']['HRV']['RR_Intervals']))
+
+        #file.write(str(ecg['ECG']['Cardiac_Cycles']))
+        #print(type(ecg['ECG']['Cardiac_Cycles']))
+
+        #file.write(ecg['ECG']['Cardiac_Cycles'].to_csv())
+
+    # Plot the processed dataframe, normalizing all variables for viewing purpose
+    """
+    """
+    bio = nk.bio_process(ecg=chest_data_dict["ECG"][baseline].flatten(), rsp=chest_data_dict['Resp'][baseline].flatten()
+                         , eda=chest_data_dict["EDA"][baseline].flatten(), sampling_rate=700)
+    #nk.z_score(bio["df"]).plot()
+
+    print(bio["ECG"].keys())
+    print(bio["EDA"].keys())
+    print(bio["RSP"].keys())
+
+    #ECG
+    print(bio["ECG"]["HRV"])
+    print(bio["ECG"]["R_Peaks"])
+
+    #EDA
+    print(bio["EDA"]["SCR_Peaks_Amplitudes"])
+    print(bio["EDA"]["SCR_Onsets"])
+
+
+    #RSP
+    print(bio["RSP"]["Cycles_Onsets"])
+    print(bio["RSP"]["Cycles_Length"])
+    """
+    print("Read data file")
+    #Flow: Read data for all subjects -> Extract features (Preprocessing) -> Train the model
+