Python mne.read_source_estimate函数代码示例

def Ara_contr(evt_list, tmin, tmax, conf_type, stcs_path, n_subjects=14, template='fsaverage'):
    con_stcs = []
    for evt in evt_list[:2]:
        fn_stc_list1 = glob.glob(subjects_dir+'/fsaverage/dSPM_ROIs/*[0-9]/*fibp1-45,evtW_%s_bc-lh.stc' %evt)
        for fn_stc1 in fn_stc_list1[:n_subjects]:
            stc1 = mne.read_source_estimate(fn_stc1, subject=template)
            stc1.crop(tmin, tmax)
            con_stcs.append(stc1.data)
    cons = np.array(con_stcs).transpose(1,2,0) 
    
    #tmin = stc1.tmin 
    tstep = stc1.tstep 
    fsave_vertices = stc1.vertices
    del stc1
   
    incon_stcs = []
    for evt in evt_list[2:]:
        fn_stc_list2 = glob.glob(subjects_dir+'/fsaverage/dSPM_ROIs/*[0-9]/*fibp1-45,evtW_%s_bc-lh.stc' %evt)
        for fn_stc2 in fn_stc_list2[:n_subjects]:
            stc2 = mne.read_source_estimate(fn_stc2, subject=template)
            stc2.crop(tmin, tmax)
            incon_stcs.append(stc2.data)
    incons = np.array(incon_stcs).transpose(1,2,0)  
    del stc2
    X = [cons[:, :, :], incons[:, :, :]]
    #import pdb
    #pdb.set_trace()
    # save data matrix
    X = np.array(X).transpose(1,2,3,0)
    X = np.abs(X)  # only magnitude
    np.savez(stcs_path + '%s.npz' %conf_type, X=X, tstep=tstep, fsave_vertices=fsave_vertices)
    return tstep, fsave_vertices, X

def test_volume_stc():
    """Test volume STCs
    """
    N = 100
    data = np.arange(N)[:, np.newaxis]
    datas = [data, data, np.arange(2)[:, np.newaxis]]
    vertno = np.arange(N)
    vertnos = [vertno, vertno[:, np.newaxis], np.arange(2)[:, np.newaxis]]
    vertno_reads = [vertno, vertno, np.arange(2)]
    for data, vertno, vertno_read in zip(datas, vertnos, vertno_reads):
        stc = VolSourceEstimate(data, vertno, 0, 1)
        fname_temp = op.join(tempdir, 'temp-vl.stc')
        stc_new = stc
        for _ in xrange(2):
            stc_new.save(fname_temp)
            stc_new = read_source_estimate(fname_temp)
            assert_true(isinstance(stc_new, VolSourceEstimate))
            assert_array_equal(vertno_read, stc_new.vertno)
            assert_array_almost_equal(stc.data, stc_new.data)
    # now let's actually read a MNE-C processed file
    stc = read_source_estimate(fname_vol, 'sample')
    assert_true(isinstance(stc, VolSourceEstimate))

    assert_true('sample' in repr(stc))
    stc_new = stc
    assert_raises(ValueError, stc.save, fname_vol, ftype='whatever')
    for _ in xrange(2):
        fname_temp = op.join(tempdir, 'temp-vol.w')
        stc_new.save(fname_temp, ftype='w')
        stc_new = read_source_estimate(fname_temp)
        assert_true(isinstance(stc_new, VolSourceEstimate))
        assert_array_equal(stc.vertno, stc_new.vertno)
        assert_array_almost_equal(stc.data, stc_new.data)

    # save the stc as a nifti file and export
    try:
        import nibabel as nib
        src = read_source_spaces(fname_vsrc)
        vol_fname = op.join(tempdir, 'stc.nii.gz')
        stc.save_as_volume(vol_fname, src,
                           dest='surf', mri_resolution=False)
        img = nib.load(vol_fname)
        assert_true(img.shape == src[0]['shape'] + (len(stc.times),))

        t1_img = nib.load(fname_t1)
        stc.save_as_volume(op.join(tempdir, 'stc.nii.gz'), src,
                           dest='mri', mri_resolution=True)
        img = nib.load(vol_fname)
        assert_true(img.shape == t1_img.shape + (len(stc.times),))
        assert_array_almost_equal(img.get_affine(), t1_img.get_affine(),
                                  decimal=5)

        # export without saving
        img = stc.as_volume(src, dest='mri', mri_resolution=True)
        assert_true(img.shape == t1_img.shape + (len(stc.times),))
        assert_array_almost_equal(img.get_affine(), t1_img.get_affine(),
                                  decimal=5)

    except ImportError:
        print 'Save as nifti test skipped, needs NiBabel'

def test_volume_stc():
    """Test volume STCs."""
    tempdir = _TempDir()
    N = 100
    data = np.arange(N)[:, np.newaxis]
    datas = [data, data, np.arange(2)[:, np.newaxis]]
    vertno = np.arange(N)
    vertnos = [vertno, vertno[:, np.newaxis], np.arange(2)[:, np.newaxis]]
    vertno_reads = [vertno, vertno, np.arange(2)]
    for data, vertno, vertno_read in zip(datas, vertnos, vertno_reads):
        stc = VolSourceEstimate(data, vertno, 0, 1)
        fname_temp = op.join(tempdir, 'temp-vl.stc')
        stc_new = stc
        for _ in range(2):
            stc_new.save(fname_temp)
            stc_new = read_source_estimate(fname_temp)
            assert (isinstance(stc_new, VolSourceEstimate))
            assert_array_equal(vertno_read, stc_new.vertices)
            assert_array_almost_equal(stc.data, stc_new.data)

    # now let's actually read a MNE-C processed file
    stc = read_source_estimate(fname_vol, 'sample')
    assert (isinstance(stc, VolSourceEstimate))

    assert ('sample' in repr(stc))
    stc_new = stc
    pytest.raises(ValueError, stc.save, fname_vol, ftype='whatever')
    for ftype in ['w', 'h5']:
        for _ in range(2):
            fname_temp = op.join(tempdir, 'temp-vol.%s' % ftype)
            stc_new.save(fname_temp, ftype=ftype)
            stc_new = read_source_estimate(fname_temp)
            assert (isinstance(stc_new, VolSourceEstimate))
            assert_array_equal(stc.vertices, stc_new.vertices)
            assert_array_almost_equal(stc.data, stc_new.data)

def test_volume_stc():
    """Test reading and writing volume STCs
    """
    N = 100
    data = np.arange(N)[:, np.newaxis]
    datas = [data, data, np.arange(2)[:, np.newaxis]]
    vertno = np.arange(N)
    vertnos = [vertno, vertno[:, np.newaxis], np.arange(2)[:, np.newaxis]]
    vertno_reads = [vertno, vertno, np.arange(2)]
    for data, vertno, vertno_read in zip(datas, vertnos, vertno_reads):
        stc = SourceEstimate(data, vertno, 0, 1)
        assert_true(stc.is_surface() is False)
        fname_temp = op.join(tempdir, 'temp-vl.stc')
        stc_new = stc
        for _ in xrange(2):
            stc_new.save(fname_temp)
            stc_new = read_source_estimate(fname_temp)
            assert_true(stc_new.is_surface() is False)
            assert_array_equal(vertno_read, stc_new.vertno)
            assert_array_almost_equal(stc.data, stc_new.data)
    # now let's actually read a MNE-C processed file
    stc = read_source_estimate(fname_vol, 'sample')
    assert_true('sample' in repr(stc))
    stc_new = stc
    assert_raises(ValueError, stc.save, fname_vol, ftype='whatever')
    for _ in xrange(2):
        fname_temp = op.join(tempdir, 'temp-vol.w')
        stc_new.save(fname_temp, ftype='w')
        stc_new = read_source_estimate(fname_temp)
        assert_true(stc_new.is_surface() is False)
        assert_array_equal(stc.vertno, stc_new.vertno)
        assert_array_almost_equal(stc.data, stc_new.data)

def apply_norm(fn_stc, event, thr=95):        
    fn_list = get_files_from_list(fn_stc)
    for fname in fn_list:
        fn_path = os.path.split(fname)[0]
        stc = mne.read_source_estimate(fname)       
        name = os.path.basename(fname)
        subject = name.split('_')[0]
        fn_base = fn_path + '/%s_%s_baseline-lh.stc' %(subject,event)
        stc = mne.read_source_estimate(fname)
        stc_base = mne.read_source_estimate(fn_base)
        thre = np.percentile(stc_base.data, thr, axis=-1)
        data = stc.data
        cal_mean = data.mean(axis=-1)
        norm_data = (data.T / thre) - 1
        norm_data[norm_data < 0] = 0
        norm_data = norm_data.T
        norm_data[cal_mean == 0, :] = 0
        norm_mean = norm_data.mean(axis=-1)
        zc_data = norm_data.T/norm_data.max(axis=-1)
        zc_data = zc_data.T
        zc_data[norm_mean == 0, :] = 0
        #import pdb
        #pdb.set_trace()
        #print zc_data.min()
        stc.data.setfield(zc_data, np.float32)
        fn_nr = fname[:fname.rfind('-lh')] + '_norm_1'
        stc.save(fn_nr, ftype='stc') 

def test_stc_methods():
    """Test stc methods lh_data, rh_data, bin(), center_of_mass(), resample()
    """
    fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-meg')
    stc = read_source_estimate(fname)

    # lh_data / rh_data
    assert_array_equal(stc.lh_data, stc.data[:len(stc.lh_vertno)])
    assert_array_equal(stc.rh_data, stc.data[len(stc.lh_vertno):])

    # bin
    bin = stc.bin(.12)
    a = np.array((1,), dtype=stc.data.dtype)
    a[0] = np.mean(stc.data[0, stc.times < .12])
    assert a[0] == bin.data[0, 0]

    assert_raises(ValueError, stc.center_of_mass, 'sample')
    stc.lh_data[:] = 0
    vertex, hemi, t = stc.center_of_mass('sample')
    assert_true(hemi == 1)
    # XXX Should design a fool-proof test case, but here were the results:
    assert_true(vertex == 92717)
    assert_true(np.round(t, 3) == 0.123)

    stc = read_source_estimate(fname)
    stc_new = deepcopy(stc)
    o_sfreq = 1.0 / stc.tstep
    # note that using no padding for this STC actually reduces edge ringing...
    stc_new.resample(2 * o_sfreq, npad=0, n_jobs=2)
    assert_true(stc_new.data.shape[1] == 2 * stc.data.shape[1])
    assert_true(stc_new.tstep == stc.tstep / 2)
    stc_new.resample(o_sfreq, npad=0)
    assert_true(stc_new.data.shape[1] == stc.data.shape[1])
    assert_true(stc_new.tstep == stc.tstep)
    assert_array_almost_equal(stc_new.data, stc.data, 5)

def load_data(stcs1_fname, stcs2_fname, dec):
    stcs1 = [mne.read_source_estimate(fname) for fname in stcs1_fname]
    stcs2 = [mne.read_source_estimate(fname) for fname in stcs2_fname]

	#This is just resampling in time, not space
    def resample_stc(stc, dec):
        """Resample stc inplace"""
        stc.data = stc.data[:,::dec].astype(np.float)
        stc.tstep *= dec
        stc.times = stc.times[::dec]
	
    if dec is not None:
        for stc in stcs1 + stcs2:
            resample_stc(stc, dec=dec)
            #stc.crop(.3,.5)
            stc.crop(0.1, None)  #cropping the time-window for faster runtime
   # print "Jane here"


    def average_stcs(stcs):
        mean_stc = copy.deepcopy(stcs[0])
        times = mean_stc.times
        n_sources, n_times = mean_stc.data.shape
        X = np.empty((len(stcs), n_sources, n_times))
        for i, stc in enumerate(stcs):
            if len(times) == len(stc.times):
                X[i] = stc.data
        mean_stc._data = np.mean(X, axis=0)
        return mean_stc, X
        print "Jane here"
	#X1, X2 are the full time,vertices,subject matrices; mean_stc1 and mean_stc2 are the grand-avgs
    mean_stc1, X1 = average_stcs(stcs1)
    mean_stc2, X2 = average_stcs(stcs2)
    return mean_stc1, X1, mean_stc2, X2

def test_sensitivity_maps():
    """Test sensitivity map computation"""
    fwd = mne.read_forward_solution(fwd_fname, surf_ori=True)
    proj_eog = read_proj(eog_fname)
    decim = 6
    for ch_type in ['eeg', 'grad', 'mag']:
        w = read_source_estimate(sensmap_fname % (ch_type, 'lh')).data
        stc = sensitivity_map(fwd, projs=None, ch_type=ch_type,
                              mode='free', exclude='bads')
        assert_array_almost_equal(stc.data, w, decim)
        assert_true(stc.subject == 'sample')
        # let's just make sure the others run
        if ch_type == 'grad':
            # fixed (2)
            w = read_source_estimate(sensmap_fname % (ch_type, '2-lh')).data
            stc = sensitivity_map(fwd, projs=None, mode='fixed',
                                  ch_type=ch_type, exclude='bads')
            assert_array_almost_equal(stc.data, w, decim)
        if ch_type == 'mag':
            # ratio (3)
            w = read_source_estimate(sensmap_fname % (ch_type, '3-lh')).data
            stc = sensitivity_map(fwd, projs=None, mode='ratio',
                                  ch_type=ch_type, exclude='bads')
            assert_array_almost_equal(stc.data, w, decim)
        if ch_type == 'eeg':
            # radiality (4), angle (5), remaining (6), and  dampening (7)
            modes = ['radiality', 'angle', 'remaining', 'dampening']
            ends = ['4-lh', '5-lh', '6-lh', '7-lh']
            for mode, end in zip(modes, ends):
                w = read_source_estimate(sensmap_fname % (ch_type, end)).data
                stc = sensitivity_map(fwd, projs=proj_eog, mode=mode,
                                      ch_type=ch_type, exclude='bads')
                assert_array_almost_equal(stc.data, w, decim)

def Ara_contr_base(evt_list, tmin, tmax, conf_type, stcs_path, n_subjects=14, template='fsaverage'):
    stcs = []
    bs_stcs = []
    for evt in evt_list:
        fn_stc_list1 = glob.glob(subjects_dir+'/fsaverage/dSPM_ROIs/*[0-9]/*fibp1-45,evtW_%s_bc-lh.stc' %evt)
        for fn_stc1 in fn_stc_list1[:n_subjects]:
            #fn_stc2 = fn_stc1.split(evt)[0] + evt[:2] +  fn_stc1.split(evt)[1]
            name = os.path.basename(fn_stc1)
            fn_path = os.path.split(fn_stc1)[0]
            subject = name.split('_')[0]
            fn_stc2 = fn_path + '/%s_%s_baseline-lh.stc' % (subject, evt[:2])
            stc1 = mne.read_source_estimate(fn_stc1, subject=template)
            stc1.crop(tmin, tmax)
            stcs.append(stc1.data)
            stc2 = mne.read_source_estimate(fn_stc2, subject=template)
            bs_stcs.append(stc2.data)
    stcs_ = np.array(stcs).transpose(1,2,0) 
    bsstcs = np.array(bs_stcs).transpose(1,2,0)
    #tmin = stc1.tmin 
    tstep = stc1.tstep 
    fsave_vertices = stc1.vertices
    del stc1, stc2
    X = [stcs_[:, :, :], bsstcs[:, :, :]]
    #import pdb
    #pdb.set_trace()
    # save data matrix
    X = np.array(X).transpose(1,2,3,0)
    X = np.abs(X)  # only magnitude
    np.savez(stcs_path + '%s.npz' %conf_type, X=X, tstep=tstep, fsave_vertices=fsave_vertices)
    return tstep, fsave_vertices, X  

def test_stc_methods():
    """Test stc methods lh_data, rh_data, bin, center_of_mass, resample"""
    stc = read_source_estimate(fname_stc)

    # lh_data / rh_data
    assert_array_equal(stc.lh_data, stc.data[:len(stc.lh_vertno)])
    assert_array_equal(stc.rh_data, stc.data[len(stc.lh_vertno):])

    # bin
    bin = stc.bin(.12)
    a = np.array((1,), dtype=stc.data.dtype)
    a[0] = np.mean(stc.data[0, stc.times < .12])
    assert a[0] == bin.data[0, 0]

    assert_raises(ValueError, stc.center_of_mass, 'sample')
    assert_raises(TypeError, stc.center_of_mass, 'sample',
                  subjects_dir=subjects_dir, surf=1)
    stc.lh_data[:] = 0
    vertex, hemi, t = stc.center_of_mass('sample', subjects_dir=subjects_dir)
    assert_true(hemi == 1)
    # XXX Should design a fool-proof test case, but here were the results:
    assert_equal(vertex, 124791)
    assert_equal(np.round(t, 2), 0.12)

    stc = read_source_estimate(fname_stc)
    stc.subject = 'sample'
    label_lh = read_labels_from_annot('sample', 'aparc', 'lh',
                                      subjects_dir=subjects_dir)[0]
    label_rh = read_labels_from_annot('sample', 'aparc', 'rh',
                                      subjects_dir=subjects_dir)[0]
    label_both = label_lh + label_rh
    for label in (label_lh, label_rh, label_both):
        assert_true(isinstance(stc.shape, tuple) and len(stc.shape) == 2)
        stc_label = stc.in_label(label)
        if label.hemi != 'both':
            if label.hemi == 'lh':
                verts = stc_label.vertices[0]
            else:  # label.hemi == 'rh':
                verts = stc_label.vertices[1]
            n_vertices_used = len(label.get_vertices_used(verts))
            assert_equal(len(stc_label.data), n_vertices_used)
    stc_lh = stc.in_label(label_lh)
    assert_raises(ValueError, stc_lh.in_label, label_rh)
    label_lh.subject = 'foo'
    assert_raises(RuntimeError, stc.in_label, label_lh)

    stc_new = deepcopy(stc)
    o_sfreq = 1.0 / stc.tstep
    # note that using no padding for this STC reduces edge ringing...
    stc_new.resample(2 * o_sfreq, npad=0, n_jobs=2)
    assert_true(stc_new.data.shape[1] == 2 * stc.data.shape[1])
    assert_true(stc_new.tstep == stc.tstep / 2)
    stc_new.resample(o_sfreq, npad=0)
    assert_true(stc_new.data.shape[1] == stc.data.shape[1])
    assert_true(stc_new.tstep == stc.tstep)
    assert_array_almost_equal(stc_new.data, stc.data, 5)

def test_stc_methods():
    """Test stc methods lh_data, rh_data, bin(), resample()."""
    stc_ = read_source_estimate(fname_stc)

    # Make a vector version of the above source estimate
    x = stc_.data[:, np.newaxis, :]
    yz = np.zeros((x.shape[0], 2, x.shape[2]))
    vec_stc_ = VectorSourceEstimate(
        np.concatenate((x, yz), 1),
        stc_.vertices, stc_.tmin, stc_.tstep, stc_.subject
    )

    for stc in [stc_, vec_stc_]:
        # lh_data / rh_data
        assert_array_equal(stc.lh_data, stc.data[:len(stc.lh_vertno)])
        assert_array_equal(stc.rh_data, stc.data[len(stc.lh_vertno):])

        # bin
        binned = stc.bin(.12)
        a = np.mean(stc.data[..., :np.searchsorted(stc.times, .12)], axis=-1)
        assert_array_equal(a, binned.data[..., 0])

        stc = read_source_estimate(fname_stc)
        stc.subject = 'sample'
        label_lh = read_labels_from_annot('sample', 'aparc', 'lh',
                                          subjects_dir=subjects_dir)[0]
        label_rh = read_labels_from_annot('sample', 'aparc', 'rh',
                                          subjects_dir=subjects_dir)[0]
        label_both = label_lh + label_rh
        for label in (label_lh, label_rh, label_both):
            assert (isinstance(stc.shape, tuple) and len(stc.shape) == 2)
            stc_label = stc.in_label(label)
            if label.hemi != 'both':
                if label.hemi == 'lh':
                    verts = stc_label.vertices[0]
                else:  # label.hemi == 'rh':
                    verts = stc_label.vertices[1]
                n_vertices_used = len(label.get_vertices_used(verts))
                assert_equal(len(stc_label.data), n_vertices_used)
        stc_lh = stc.in_label(label_lh)
        pytest.raises(ValueError, stc_lh.in_label, label_rh)
        label_lh.subject = 'foo'
        pytest.raises(RuntimeError, stc.in_label, label_lh)

        stc_new = deepcopy(stc)
        o_sfreq = 1.0 / stc.tstep
        # note that using no padding for this STC reduces edge ringing...
        stc_new.resample(2 * o_sfreq, npad=0, n_jobs=2)
        assert (stc_new.data.shape[1] == 2 * stc.data.shape[1])
        assert (stc_new.tstep == stc.tstep / 2)
        stc_new.resample(o_sfreq, npad=0)
        assert (stc_new.data.shape[1] == stc.data.shape[1])
        assert (stc_new.tstep == stc.tstep)
        assert_array_almost_equal(stc_new.data, stc.data, 5)

def test_io_w():
    """Test IO for w files
    """
    stc = _fake_stc(n_time=1)
    w_fname = op.join(tempdir, 'fake')
    stc.save(w_fname, ftype='w')
    src = read_source_estimate(w_fname)
    src.save(op.join(tempdir, 'tmp'), ftype='w')
    src2 = read_source_estimate(op.join(tempdir, 'tmp-lh.w'))
    assert_array_almost_equal(src.data, src2.data)
    assert_array_almost_equal(src.lh_vertno, src2.lh_vertno)
    assert_array_almost_equal(src.rh_vertno, src2.rh_vertno)

def test_morph_data():
    """Test morphing of data
    """
    subject_from = 'sample'
    subject_to = 'fsaverage'
    fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-meg')
    stc_from = read_source_estimate(fname)
    stc_from.crop(0.09, 0.1)  # for faster computation
    # After running this:
    #    stc_from.save('%s_audvis-meg-cropped' % subject_from)
    # this was run from a command line:
    #    mne_make_movie --stcin sample_audvis-meg-cropped-lh.stc
    #        --subject sample --morph fsaverage --smooth 12 --morphgrade 3
    #        --stc fsaverage_audvis-meg-cropped
    # XXX These files should eventually be moved to the sample dataset and
    # removed from mne/fiff/tests/data/
    fname = op.join(op.dirname(__file__), '..', 'fiff', 'tests', 'data',
                    'fsaverage_audvis-meg-cropped')
    stc_to = read_source_estimate(fname)
    stc_to1 = morph_data(subject_from, subject_to, stc_from,
                            grade=3, smooth=12, buffer_size=1000)
    stc_to1.save('%s_audvis-meg' % subject_to)
    stc_to2 = morph_data(subject_from, subject_to, stc_from,
                            grade=3, smooth=12, buffer_size=3)
    # indexing silliness here due to mne_make_movie's indexing oddities
    assert_array_almost_equal(stc_to.data, stc_to1.data[:, 0][:, None], 5)
    assert_array_almost_equal(stc_to1.data, stc_to2.data)
    # make sure precomputed morph matrices work
    vertices_to = grade_to_vertices(subject_to, grade=3)
    morph_mat = compute_morph_matrix(subject_from, subject_to,
                                     stc_from.vertno, vertices_to,
                                     smooth=12)
    stc_to3 = morph_data_precomputed(subject_from, subject_to,
                                     stc_from, vertices_to, morph_mat)
    assert_array_almost_equal(stc_to1.data, stc_to3.data)

    mean_from = stc_from.data.mean(axis=0)
    mean_to = stc_to1.data.mean(axis=0)
    assert_true(np.corrcoef(mean_to, mean_from).min() > 0.999)

    # test two types of morphing:
    # 1) make sure we can fill by morphing
    stc_to5 = morph_data(subject_from, subject_to, stc_from,
                            grade=None, smooth=12, buffer_size=3)
    assert_true(stc_to5.data.shape[0] == 163842 + 163842)

    # 2) make sure we can specify vertices
    vertices_to = [np.arange(10242), np.arange(10242)]
    stc_to3 = morph_data(subject_from, subject_to, stc_from,
                            grade=vertices_to, smooth=12, buffer_size=3)
    stc_to4 = morph_data(subject_from, subject_to, stc_from,
                            grade=5, smooth=12, buffer_size=3)
    assert_array_almost_equal(stc_to3.data, stc_to4.data)

def test_morph_data():
    """Test morphing of data
    """
    subject_from = 'sample'
    subject_to = 'fsaverage'
    fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-meg')
    stc_from = read_source_estimate(fname, subject='sample')
    fname = op.join(data_path, 'MEG', 'sample', 'fsaverage_audvis-meg')
    stc_to = read_source_estimate(fname)
    # make sure we can specify grade
    stc_from.crop(0.09, 0.1)  # for faster computation
    stc_to.crop(0.09, 0.1)  # for faster computation
    stc_to1 = stc_from.morph(subject_to, grade=3, smooth=12, buffer_size=1000,
                             subjects_dir=subjects_dir)
    stc_to1.save(op.join(tempdir, '%s_audvis-meg' % subject_to))
    # make sure we can specify vertices
    vertices_to = grade_to_vertices(subject_to, grade=3)
    stc_to2 = morph_data(subject_from, subject_to, stc_from,
                         grade=vertices_to, smooth=12, buffer_size=1000,
                         subjects_dir=subjects_dir)
    # make sure we can use different buffer_size
    stc_to3 = morph_data(subject_from, subject_to, stc_from,
                         grade=vertices_to, smooth=12, buffer_size=3,
                         subjects_dir=subjects_dir)

    assert_array_almost_equal(stc_to.data, stc_to1.data, 5)
    assert_array_almost_equal(stc_to1.data, stc_to2.data)
    assert_array_almost_equal(stc_to1.data, stc_to3.data)
    # make sure precomputed morph matrices work
    morph_mat = compute_morph_matrix(subject_from, subject_to,
                                     stc_from.vertno, vertices_to,
                                     smooth=12, subjects_dir=subjects_dir)
    stc_to3 = stc_from.morph_precomputed(subject_to, vertices_to, morph_mat)
    assert_array_almost_equal(stc_to1.data, stc_to3.data)

    mean_from = stc_from.data.mean(axis=0)
    mean_to = stc_to1.data.mean(axis=0)
    assert_true(np.corrcoef(mean_to, mean_from).min() > 0.999)

    # make sure we can fill by morphing
    stc_to5 = morph_data(subject_from, subject_to, stc_from, grade=None,
                         smooth=12, buffer_size=3, subjects_dir=subjects_dir)
    assert_true(stc_to5.data.shape[0] == 163842 + 163842)

    # test morphing to the same subject
    stc_to6 = stc_from.morph(subject_from, grade=stc_from.vertno, smooth=1,
                             subjects_dir=subjects_dir)
    mask = np.ones(stc_from.data.shape[0], dtype=np.bool)
    # XXX: there is a bug somewhere that causes a difference at 2 vertices..
    mask[6799] = False
    mask[6800] = False
    assert_array_almost_equal(stc_from.data[mask], stc_to6.data[mask], 5)

def Ara_contr(evt_list, tmin, tmax, conf_type, out_path, n_subjects=14,
              template='fsaverage', subjects_dir=None):

    ''' Prepare arrays for the contrasts of conflicts perception
        and conflicts response.

        Parameter
        ---------
        evt_list: list
            The events list.
        tmin, tmax: float (s)
            The time period of data.
        conf_type: string
            The type of contrasts,'conf_per' or 'conf_res'
        out_path: string
            The path to store aranged arrays.
        n_subjects: int
            The amount subjects.
        subjects_dir: The total bath of all the subjects.
    '''
    con_stcs = []
    for evt in evt_list[:2]:
        fn_stc_list1 = glob.glob(subjects_dir + '/fsaverage/dSPM_ROIs/*[0-9]/*fibp1-45,evtW_%s_bc-lh.st-lh.stc' % evt)
        for fn_stc1 in fn_stc_list1[:n_subjects]:
            stc1 = mne.read_source_estimate(fn_stc1, subject=template)
            stc1.crop(tmin, tmax)
            con_stcs.append(stc1.data)
    cons = np.array(con_stcs).transpose(1, 2, 0)

    # tmin = stc1.tmin
    tstep = stc1.tstep
    fsave_vertices = stc1.vertices
    del stc1

    incon_stcs = []
    for evt in evt_list[2:]:
        fn_stc_list2 = glob.glob(subjects_dir + '/fsaverage/dSPM_ROIs/*[0-9]/*fibp1-45,evtW_%s_bc-lh.st-lh.stc' % evt)
        for fn_stc2 in fn_stc_list2[:n_subjects]:
            stc2 = mne.read_source_estimate(fn_stc2, subject=template)
            stc2.crop(tmin, tmax)
            incon_stcs.append(stc2.data)
    incons = np.array(incon_stcs).transpose(1, 2, 0)
    del stc2
    X = [cons[:, :, :], incons[:, :, :]]
    # import pdb
    # pdb.set_trace()
    # save data matrix
    X = np.array(X).transpose(1, 2, 3, 0)
    X = np.abs(X)  # only magnitude
    np.savez(out_path + '%s.npz' % conf_type, X=X, tstep=tstep,
             fsave_vertices=fsave_vertices)
    return tstep, fsave_vertices, X