discrete_wavelet.py

import numpy as np
import statistics
import pywt
import csv
from scipy.stats import kurtosis

# Reading file and saving it in a variable, suppose x;
x = np.array([])
filename = '/User/'
with open(filename) as csvfile:
    f_read = csv.reader(csvfile, delimiter = ',')
    for row in f_read:
        x = np.append(x, [float(j) for j in row])

[a, d1, d2, d3, d4] = pywt.wavedec(x, 'haar', level=4)

def comp_moment(feature):
    step = int(len(feature)/2)
    avg_temp = np.zeros([2])
    stn_dev_temp = np.zeros([2])
    kurto_temp = np.zeros([2])
    for i in range(int(len(feature)/step)):
        avg_temp[i] = np.mean(feature[step*i:step*(i+1)])
        stn_dev_temp[i] = statistics.stdev(feature[step*i:step*(i+1)])
        kurto_temp[i] = kurtosis(feature[step*i:step*(i+1)])
        return (avg_temp, stn_dev_temp, kurto_temp)


    #Approximation coeficient
    avg_temp, stn_dev_temp, kurto_temp = comp_moment(a)
    avg = avg_temp
    stn_dev = stn_dev_temp
    kurto = kurto_temp

    # d1 coffiecient
    avg_temp, stn_dev_temp, kurto_temp = comp_moment(d1)
    avg = np.append(avg, avg_temp)
    stn_dev = np.append(stn_dev, stn_dev_temp)
    kurto = np.append(kurto, kurto_temp)

    # d2 coffiecient
    avg_temp, stn_dev_temp, kurto_temp = comp_moment(d2)
    avg = np.append(avg, avg_temp)
    stn_dev = np.append(stn_dev, stn_dev_temp)
    kurto = np.append(kurto, kurto_temp)

    # d3 coffiecient
    avg_temp, stn_dev_temp, kurto_temp = comp_moment(d3)
    avg = np.append(avg, avg_temp)
    stn_dev = np.append(stn_dev, stn_dev_temp)
    kurto = np.append(kurto, kurto_temp)

    # d4 coffiecient
    avg_temp, stn_dev_temp, kurto_temp = comp_moment(d4)
    avg = np.append(avg, avg_temp)
    stn_dev = np.append(stn_dev, stn_dev_temp)
    kurto = np.append(kurto, kurto_temp)

    feature_dwt = np.append(np.append(avg, stn_dev), kurto)