In [1]:

    

%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os
import random


    

In [2]:

    

import sys
sys.path.append('..')


    

In [37]:

    

target = 'Patient_2'
data_type = 'preictal' # interictal, test
segment = 3


    

In [23]:

    

target = 'Dog_5'
data_type = 'test' # preictal interictal, test
segment = 3


    

In [38]:

    

fname = '../seizure-data/%s/%s_%s_segment_%04d.mat'%(target,target,data_type,segment)


    

In [39]:

    

import scipy.io
data = scipy.io.loadmat(fname)


    

In [40]:

    

data.keys()


    

    
Out[40]:





['__version__', 'preictal_segment_3', '__header__', '__globals__']

In [41]:

    

k = '%s_segment_%d'%(data_type,segment)


    

In [42]:

    

data_length_sec = data[k]['data_length_sec'][0,0][0,0]
sequence = data[k]['sequence'][0,0][0,0]
Fs = float(data[k]['sampling_frequency'][0,0][0,0])

data_length_sec, sequence, Fs


    

    
Out[42]:





(600, 3, 5000.0)

In [43]:

    

channels = [t[0] for t in data[k]['channels'][0,0][0]]
len(channels),channels


    

    
Out[43]:





(24,
 [u'LTG_01',
  u'LTG_02',
  u'LTG_03',
  u'LTG_04',
  u'LTG_05',
  u'LTG_06',
  u'LTG_07',
  u'LTG_08',
  u'LTG_09',
  u'LTG_10',
  u'LTG_11',
  u'LTG_12',
  u'LTG_13',
  u'LTG_14',
  u'LTG_15',
  u'LTG_16',
  u'LTG_17',
  u'LTG_18',
  u'LTG_19',
  u'LTG_20',
  u'LTG_21',
  u'LTG_22',
  u'LTG_23',
  u'LTG_24'])

In [183]:

    

d = data[k]['data'][0,0]
d.shape


    

    
Out[183]:





(15, 3000000)

In [184]:

    

assert len(channels) == d.shape[0]


    

In [185]:

    

assert int(Fs*data_length_sec + 0.5) == d.shape[1],int(Fs*data_length_sec + 0.5)


    

In [186]:

    

Nchannels = 4 # d.shape[0]
for i in range(Nchannels):
    pl.subplot(Nchannels,1,i+1)
    pl.plot(d[i,:500000])
    pl.title(channels[i])
pl.gcf().set_size_inches(14,8);


    

In [187]:

    

Y = np.abs(np.fft.rfft(d[0,:]))


    

In [188]:

    

Y[60*data_length_sec]/np.std(d[0,:])


    

    
Out[188]:





394058.2261422351

In [193]:

    

pl.plot(Y[:200*data_length_sec])


    

    
Out[193]:





[<matplotlib.lines.Line2D at 0x1198cf350>]






    

In [192]:

    

NFFT=8192
Pxx, freqs, bins, im = pl.specgram(d[3,:], NFFT=NFFT, pad_to=NFFT*2, Fs=Fs, noverlap=NFFT/2)
pl.gca().set_ylim((0,1000))
pl.show()