--- a
+++ b/comparacion_patologi.py
@@ -0,0 +1,110 @@
+# -*- coding: utf-8 -*-
+"""
+Matrix de caracteristicas de 5 registros PATOLOGICOS
+Created on Thu May  3 07:09:12 2018
+
+@author: Kevin Machado G.
+Ref:
+    [1] https://www.datacamp.com/community/blog/python-pandas-cheat-sheet
+"""
+import matplotlib.pyplot as plt
+
+import numpy as np
+# Data manipulation
+import pandas as pd
+# Own Library
+import ppfunctions_1 as ppf
+
+import os 
+import scipy.io.wavfile as wf
+
+# -----------------------------------------------------------------------------
+print('importing all training-a data base')
+# Looking for heart sounds data absolute path
+l1=os.path.abspath('Data Base HS\\pathologic\\2.1.wav')
+l1=l1.replace('\\','/')
+l2=os.path.abspath('Data Base HS\\pathologic\\2.3.wav')
+l2=l2.replace('\\','/')
+l3=os.path.abspath('Data Base HS\\pathologic\\3.2.wav')
+l3=l3.replace('\\','/')
+l4=os.path.abspath('Data Base HS\\pathologic\\4.3.wav')
+l4=l4.replace('\\','/')
+l5=os.path.abspath('Data Base HS\\pathologic\\4.5.wav')
+l5=l5.replace('\\','/')
+
+# reading file
+Fs1, data1 = wf.read(l1)
+Fs2, data2 = wf.read(l2)
+Fs3, data3 = wf.read(l3)
+Fs4, data4 = wf.read(l4)
+Fs5, data5 = wf.read(l5)
+
+# Clear paths
+del l1, l2, l3, l4, l5
+
+#
+
+duration1 = 1/Fs1*np.size(data1)
+duration2 = 1/Fs2*np.size(data2)
+duration3 = 1/Fs3*np.size(data3)
+duration4 = 1/Fs4*np.size(data4)
+duration5 = 1/Fs5*np.size(data5)
+
+vt1 = np.linspace(0,duration1,np.size(data1)) # Vector time 1
+vt2 = np.linspace(0,duration2,np.size(data2)) # Vector time 3
+vt3 = np.linspace(0,duration3,np.size(data3)) # Vector time 5
+vt4 = np.linspace(0,duration4,np.size(data4)) # Vector time 7
+vt5 = np.linspace(0,duration5,np.size(data5)) # Vector time 9
+
+data1 = ppf.vec_nor(data1)
+data2 = ppf.vec_nor(data2)
+data3 = ppf.vec_nor(data3)
+data4 = ppf.vec_nor(data4)
+data5 = ppf.vec_nor(data5)
+
+pcgFFT1, vTfft1 = ppf.fft_k_N(data1, Fs1, 2000)
+pcgFFT2, vTfft2 = ppf.fft_k_N(data2, Fs2, 2000)
+pcgFFT3, vTfft3 = ppf.fft_k_N(data3, Fs3, 2000)
+pcgFFT4, vTfft4 = ppf.fft_k_N(data4, Fs4, 2000)
+pcgFFT5, vTfft5 = ppf.fft_k_N(data5, Fs5, 2000)
+
+idx1 = (np.abs(vt1-5)).argmin()                # Find the index of time vector in 10 seconds
+idx2 = (np.abs(vt2-5)).argmin()                # Find the index of time vector in 10 seconds
+idx3 = (np.abs(vt3-5)).argmin()                # Find the index of time vector in 10 seconds
+idx4 = (np.abs(vt4-5)).argmin()                # Find the index of time vector in 10 seconds
+idx5 = (np.abs(vt5-5)).argmin()                # Find the index of time vector in 10 seconds
+#
+# -----------------------------------------------------------------------------
+# Energy of vibratory signal spectrum
+# 1. 0-5Hz, 2. 5-25Hz; 3. 25-120Hz; 4. 120-240Hz; 5. 240-500Hz; 6. 500-1000Hz; 7. 1000-2000Hz
+EVS1 = ppf.E_VS(pcgFFT1, vTfft1, 'percentage')
+EVS2 = ppf.E_VS(pcgFFT2, vTfft2, 'percentage')
+EVS3 = ppf.E_VS(pcgFFT3, vTfft3, 'percentage')
+EVS4 = ppf.E_VS(pcgFFT4, vTfft4, 'percentage')
+EVS5 = ppf.E_VS(pcgFFT5, vTfft5, 'percentage')
+
+# Showing Results in Pandas
+data = {'P1': np.round(EVS1),'P2': np.round(EVS2), 'P3': np.round(EVS3), 'P4': np.round(EVS4), 'P5': np.round(EVS5)}
+print('Registros Patologicos')
+df=pd.DataFrame(data,index=['Total (%)','0-5Hz','5-25Hz','25-120Hz','120-240Hz','240-500Hz','500-1kHz','1k-2kHz'],columns=['P1','P2', 'P3', 'P4', 'P5'])
+print (df)
+
+# df.to_excel('pato_5.xlsx') # writing to excel
+
+plt.figure(1)
+
+plt.subplot(5,1,1)
+plt.title('Transformada de Fourier')
+plt.plot(vTfft1, pcgFFT1,'r')
+
+plt.subplot(5,1,2)
+plt.plot(vTfft2, pcgFFT2,'r')
+
+plt.subplot(5,1,3)
+plt.plot(vTfft3, pcgFFT3,'r')
+
+plt.subplot(5,1,4)
+plt.plot(vTfft4, pcgFFT4,'r')
+
+plt.subplot(5,1,5)
+plt.plot(vTfft5, pcgFFT5,'r')