--- a
+++ b/creating_vector.py
@@ -0,0 +1,42 @@
+import csv
+from collections import defaultdict
+import numpy as np
+from scipy.signal import *
+from numpy.fft import * 
+from scipy import *
+from pylab import *
+import pywt
+fout_data = open("train.csv",'a')
+vec = []
+chan = ['Fp1','AF3','F3','F7','FC5','FC1','C3','T7','CP5','CP1','P3','P7','PO3','O1','Oz','Pz','Fp2','AF4','Fz','F4','F8','FC6','FC2','Cz','C4','T8','CP6','CP2','P4','P8','PO4','O2']
+columns = defaultdict(list) # each value in each column is appended to a list
+
+with open("features_raw.csv") as f:
+    reader = csv.DictReader(f) # read rows into a dictionary format
+    for row in reader: # read a row as {column1: value1, column2: value2,...}
+        for (k,v) in row.items(): # go over each column name and value 
+            columns[k].append(v) # append the value into the appropriate list
+                                 # based on column name k
+for i in chan:
+	x = np.array(columns[i]).astype(np.float)
+	coeffs = pywt.wavedec(x, 'db4', level=6)
+	cA6, cD6, cD5,cD4,cD3,cD2,cD1 = coeffs
+	cD5 = np.std(cD5)
+	cD4 = np.std(cD4)
+	cD3 = np.std(cD3)
+	cD2 = np.std(cD2)
+	cD1 = np.std(cD1)
+	if i =="O2":
+		fout_data.write(str(cD5)+",")
+		fout_data.write(str(cD4)+",")
+		fout_data.write(str(cD3)+",")
+		fout_data.write(str(cD2)+",")
+		fout_data.write(str(cD1))
+	else:
+		fout_data.write(str(cD5)+",")
+		fout_data.write(str(cD4)+",")
+		fout_data.write(str(cD3)+",")
+		fout_data.write(str(cD2)+",")
+		fout_data.write(str(cD1)+",")
+fout_data.write("\n")
+fout_data.close()