示例#1
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def preprocess(data, filt=None):
    # copying data
    dat = data.copy()
    fs_n = dat.fs / 2.0

    # butter filtering low
    b, a = proc.signal.butter(5, [13 / fs_n], btype='low')
    #dat = proc.filtfilt(dat, b, a)

    # butter filtering high
    b, a = proc.signal.butter(4, [9 / fs_n], btype='high')
    dat = proc.filtfilt(dat, b, a)

    # subsampling
    #dat = proc.subsample(dat, 50)

    if filt is None:
        # calculate_csp
        filt, pattern, _ = proc.calculate_csp(dat)

        # plot_csp_pattern
        #plot_csp_pattern(pattern)

    # apply_csp
    dat = proc.apply_csp(dat, filt)

    # variance and logarithm
    dat = proc.variance(dat)
    #dat = proc.logarithm(dat)
    return dat, filt
示例#2
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 def test_apply_csp(self):
     """apply_csp."""
     dat = apply_csp(self.epo, self.filter)
     # reduce the channels down to 2, the rest of the shape should
     # stay the same
     self.assertEqual(self.epo.data.shape[0], dat.data.shape[0])
     self.assertEqual(self.epo.data.shape[1], dat.data.shape[1])
     self.assertEqual(2, dat.data.shape[2])
     # new name for csp axis
     self.assertEqual(dat.names[-1], 'CSP Channel')
     # check if the dot product was calculated correctly
     d = np.array([np.dot(self.epo.data[i], self.filter[:, [0, -1]]) for i in range(self.epo.data.shape[0])])
     np.testing.assert_array_equal(d, dat.data)
示例#3
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 def test_apply_csp(self):
     """apply_csp."""
     dat = apply_csp(self.epo, self.filter)
     # reduce the channels down to 2, the rest of the shape should
     # stay the same
     self.assertEqual(self.epo.data.shape[0], dat.data.shape[0])
     self.assertEqual(self.epo.data.shape[1], dat.data.shape[1])
     self.assertEqual(2, dat.data.shape[2])
     # new name for csp axis
     self.assertEqual(dat.names[-1], 'CSP Channel')
     # check if the dot product was calculated correctly
     d = np.array([
         np.dot(self.epo.data[i], self.filter[:, [0, -1]])
         for i in range(self.epo.data.shape[0])
     ])
     np.testing.assert_array_equal(d, dat.data)
def preprocess(data, filt=None):
    dat = data.copy()
    fs_n = 250 # sample rate is 250 for us

    b, a = proc.signal.butter(5, [13 / fs_n], btype='low')
    dat = proc.filtfilt(dat, b, a)

    b, a = proc.signal.butter(5, [9 / fs_n], btype='high')
    dat = proc.filtfilt(dat, b, a)

    dat = proc.subsample(dat, 50)

    if filt is None:
        filt, pattern, _ = proc.calculate_csp(dat)
        plot_csp_pattern(pattern)
    dat = proc.apply_csp(dat, filt)

    dat = proc.variance(dat)
    dat = proc.logarithm(dat)
    return dat, filt
def preprocess(data, filt=None):
    dat = data.copy()
    fs_n = dat.fs / 2

    b, a = proc.signal.butter(5, [13 / fs_n], btype='low')
    dat = proc.filtfilt(dat, b, a)


    b, a = proc.signal.butter(5, [9 / fs_n], btype='high')
    dat = proc.filtfilt(dat, b, a)
    
    dat = proc.subsample(dat, 50)

    if filt is None:
        filt, pattern, _ = proc.calculate_csp(dat)
        plot_csp_pattern(pattern)
    dat = proc.apply_csp(dat, filt)
    
    dat = proc.variance(dat)
    dat = proc.logarithm(dat)
    return dat, filt
示例#6
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def preprocess(dat,filt=None):
    	'''fs_n = dat.fs / 2
    	b, a = proc.signal.butter(5, [13 / fs_n], btype='low')
	dat = proc.lfilter(dat, b, a)
	b, a = proc.signal.butter(5, [8 / fs_n], btype='high')
	dat = proc.lfilter(dat, b, a)
	print dat'''
   

        dat = proc.subsample(dat, 64)
        #epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL)
	
        #fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)
        #fv = proc.create_feature_vectors(dat)

        if filt is None:
        	filt, pattern, _ = proc.calculate_csp(dat)
        	#plot_csp_pattern(pattern)
    	dat = proc.apply_csp(dat, filt)
    
   	dat = proc.variance(dat)
    	dat = proc.logarithm(dat)
    	return dat, filt
示例#7
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        cD_Energy.append(abs(np.sum(np.square(cD_values[x]))))
        features.append(abs(np.sum(np.square(cD_values[x]))))

    return features


final_epoch1 = append_epo(epoch_subject1_class1, epoch_subject1_class2)
final_epoch_ch1 = append_epo(
    epoch_subject1_ch1_class1,
    epoch_subject1_ch1_class2)  #appended both the epoch data sets
w1, a1, d1 = calculate_csp(final_epoch1)
w2, a2, d2 = calculate_csp(
    final_epoch_ch1
)  #calculate csp   but why we need to append the data and calculate the csp paramters waht if we calculate it individually
fil_epoch_subject1_class1 = apply_csp(
    epoch_subject1_class1, w1,
    [0, 1, 2, 3, 4, -5, -4, -3, -2, -1
     ])  # brackets number are the column number to use
fil_epoch_subject1_class2 = apply_csp(epoch_subject1_class2, w1,
                                      [0, 1, 2, 3, 4, -5, -4, -3, -2, -1])
fil_final_epoch1 = append_epo(
    fil_epoch_subject1_class1,
    fil_epoch_subject1_class2)  # final filtered epo     class*time*channel
fil_epoch_subject1_ch1_class1 = apply_csp(
    epoch_subject1_ch1_class1, w2,
    [0, 1, 2, 3, 4, -5, -4, -3, -2, -1
     ])  # brackets number are the column number to use
fil_epoch_subject1_ch1_class2 = apply_csp(epoch_subject1_ch1_class2, w2,
                                          [0, 1, 2, 3, 4, -5, -4, -3, -2, -1])
fil_final_epoch_ch1 = append_epo(
    fil_epoch_subject1_class1,
    fil_epoch_subject1_class2)  # final filtered epo     class*time*channel
示例#8
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 def test_apply_csp_copy(self):
     """apply_csp must not modify argument."""
     cpy = self.epo.copy()
     apply_csp(self.epo, self.filter)
     self.assertEqual(self.epo, cpy)
示例#9
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 def test_apply_csp_copy(self):
     """apply_csp must not modify argument."""
     cpy = self.epo.copy()
     apply_csp(self.epo, self.filter)
     self.assertEqual(self.epo, cpy)