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()