Python preprocessing.read_ica函数代码示例

def test_n_components_and_max_pca_components_none(method):
    """Test n_components and max_pca_components=None."""
    _skip_check_picard(method)
    raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
    events = read_events(event_name)
    picks = pick_types(raw.info, eeg=True, meg=False)
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)

    max_pca_components = None
    n_components = None
    random_state = 12345

    tempdir = _TempDir()
    output_fname = op.join(tempdir, 'test_ica-ica.fif')
    ica = ICA(max_pca_components=max_pca_components, method=method,
              n_components=n_components, random_state=random_state)
    with pytest.warns(None):  # convergence
        ica.fit(epochs)
    ica.save(output_fname)

    ica = read_ica(output_fname)

    # ICA.fit() replaced max_pca_components, which was previously None,
    # with the appropriate integer value.
    assert_equal(ica.max_pca_components, epochs.info['nchan'])
    assert ica.n_components is None

def test_n_components_none():
    """Test n_components=None."""
    raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
    events = read_events(event_name)
    picks = pick_types(raw.info, eeg=True, meg=False)
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)

    max_pca_components = 10
    n_components = None
    random_state = 12345

    tempdir = _TempDir()
    output_fname = op.join(tempdir, 'test_ica-ica.fif')

    ica = ICA(max_pca_components=max_pca_components,
              n_components=n_components, random_state=random_state)
    with warnings.catch_warnings(record=True):  # convergence
        ica.fit(epochs)
    ica.save(output_fname)

    ica = read_ica(output_fname)

    # ICA.fit() replaced max_pca_components, which was previously None,
    # with the appropriate integer value.
    assert_equal(ica.max_pca_components, 10)
    assert_is_none(ica.n_components)

def load_ica(subject, description, ica_data_root=None):
    if ica_data_root is None:
        # use default data root
        import deepthought
        data_root = os.path.join(deepthought.DATA_PATH, 'OpenMIIR')
        ica_data_root = os.path.join(data_root, 'eeg', 'preprocessing', 'ica')

    ica_filepath = os.path.join(ica_data_root,
                                '{}-{}-ica.fif'.format(subject, description))
    return read_ica(ica_filepath)

def cli(subjdirs):
    """
    Show the variance explained for mne ica solution

    EXAMPLES:
    
        Show explained variances for ica solutions for each subject in FIF_DATASET and write them to file vars.txt:

        $ ica_var  FIF_DATASET/*/*-ica.fif >> vars.txt

    """
    for fname in subjdirs:
        with nostdout():
            ica = read_ica(fname)
        n_comp = ica.n_components_
        tot_var = ica.pca_explained_variance_.sum()
        n_comp_var = ica.pca_explained_variance_[:n_comp].sum()
        PVE = n_comp_var / tot_var
        click.echo(PVE)

#noise_cov_er.save(empty_room_fname[:-4]+'-cov.fif')
###############################################################################
# 1) Fit ICA model using the FastICA algorithm

# Other available choices are `infomax` or `extended-infomax`
# We pass a float value between 0 and 1 to select n_components based on the
# percentage of variance explained by the PCA components.

raw = Raw(raw_fname, preload=True)

picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False, ecg=False,
                       stim=False, exclude='bads')
# maximum number of components to reject
n_max_ecg, n_max_eog = 2, 3  # here we expect horizontal EOG components
try:
    ica = read_ica(raw_data_folder + '/ica_pre.fif')
except:
    ica = ICA(n_components=0.95, max_pca_components = 64, method='fastica')
        #noise_cov = noise_cov_er)

    #ica.fit(raw, picks=picks, decim=3, reject=dict(mag=4e-12, grad=4000e-13))
    ica.fit(raw, picks=picks, decim = 5, reject=dict(mag=4e-11, grad=4000e-12))
    # To save an ICA solution you can say:

###############################################################################
# 2) identify bad components by analyzing latent sources.

title = 'Sources related to %s artifacts (red)'

# generate ECG epochs use detection via phase statistics

def test_ica_additional(method):
    """Test additional ICA functionality."""
    _skip_check_picard(method)

    tempdir = _TempDir()
    stop2 = 500
    raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
    raw.del_proj()  # avoid warnings
    raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
    # XXX This breaks the tests :(
    # raw.info['bads'] = [raw.ch_names[1]]
    test_cov = read_cov(test_cov_name)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')[1::2]
    epochs = Epochs(raw, events, None, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True, proj=False)
    epochs.decimate(3, verbose='error')
    assert len(epochs) == 4

    # test if n_components=None works
    ica = ICA(n_components=None, max_pca_components=None,
              n_pca_components=None, random_state=0, method=method, max_iter=1)
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(epochs)
    # for testing eog functionality
    picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
    epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
                        baseline=(None, 0), preload=True)
    del picks2

    test_cov2 = test_cov.copy()
    ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
              n_pca_components=4, method=method)
    assert (ica.info is None)
    with pytest.warns(RuntimeWarning, match='normalize_proj'):
        ica.fit(raw, picks[:5])
    assert (isinstance(ica.info, Info))
    assert (ica.n_components_ < 5)

    ica = ICA(n_components=3, max_pca_components=4, method=method,
              n_pca_components=4, random_state=0)
    pytest.raises(RuntimeError, ica.save, '')

    ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)

    # check passing a ch_name to find_bads_ecg
    with pytest.warns(RuntimeWarning, match='longer'):
        _, scores_1 = ica.find_bads_ecg(raw)
        _, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
    assert scores_1[0] != scores_2[0]

    # test corrmap
    ica2 = ica.copy()
    ica3 = ica.copy()
    corrmap([ica, ica2], (0, 0), threshold='auto', label='blinks', plot=True,
            ch_type="mag")
    corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
    assert (ica.labels_["blinks"] == ica2.labels_["blinks"])
    assert (0 in ica.labels_["blinks"])
    # test retrieval of component maps as arrays
    components = ica.get_components()
    template = components[:, 0]
    EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')

    corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True,
            ch_type="mag")
    assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])

    plt.close('all')

    ica_different_channels = ICA(n_components=2, random_state=0).fit(
        raw, picks=[2, 3, 4, 5])
    pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0))

    # test warnings on bad filenames
    ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
    with pytest.warns(RuntimeWarning, match='-ica.fif'):
        ica.save(ica_badname)
    with pytest.warns(RuntimeWarning, match='-ica.fif'):
        read_ica(ica_badname)

    # test decim
    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4, method=method, max_iter=1)
    raw_ = raw.copy()
    for _ in range(3):
        raw_.append(raw_)
    n_samples = raw_._data.shape[1]
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(raw, picks=picks[:5], decim=3)
    assert raw_._data.shape[1] == n_samples

    # test expl var
    ica = ICA(n_components=1.0, max_pca_components=4,
              n_pca_components=4, method=method, max_iter=1)
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(raw, picks=None, decim=3)
    assert (ica.n_components_ == 4)
    ica_var = _ica_explained_variance(ica, raw, normalize=True)
#.........这里部分代码省略.........

    return eve_dict, id_dict

# Set epoch parameters
tmin, tmax = -0.4, 0.6  # no need to take more than this, wide enough to see eyemov though
rej_tmin, rej_tmax = -0.2, 0.2  # reject trial only if blinks in the 400 ms middle portion!
baseline = (-0.2, 0.)
reject = dict(eog=150e-6, mag=4e-12, grad=4000e-13) # compare to standard rejection

raw_path = ad._scratch_folder + '/tsss_initial/007_SGF'
eve_path = ad._scratch_folder + '/events.fif/007_SGF/raw'

fname = raw_path + '/VS_1a_1_tsss_mc.fif'

raw = Raw(fname, preload=True)

ica = read_ica(raw_path + '/ica_pre.fif')
print 'Excluding', ica.exclude
raw_ica = ica.apply(raw, copy=True)

events = mne.read_events(eve_path + '/VS_1a_1-eve.fif')
picks = pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, misc=True)
eve_dict, id_dict = split_events_by_trialtype(events)
for trial_type in ['VS']:

    epochs = mne.Epochs(raw, eve_dict[trial_type], id_dict,
                        tmin, tmax, picks=picks, verbose=False,
                        baseline=baseline, reject=reject, preload=True,
                        reject_tmin=rej_tmin, reject_tmax=rej_tmax) # Check rejection settings
    epochs_ica = mne.Epochs(raw_ica, eve_dict[trial_type], id_dict,
                        tmin, tmax, picks=picks, verbose=False,
                        baseline=baseline, reject=None, preload=True)

def preprocess_ICA_fif_to_ts(fif_file, ECG_ch_name, EoG_ch_name, l_freq, h_freq, down_sfreq, variance, is_sensor_space, data_type):
    import os
    import numpy as np

    import mne
    from mne.io import Raw
    from mne.preprocessing import ICA, read_ica
    from mne.preprocessing import create_ecg_epochs, create_eog_epochs
    from mne.report import Report

    from nipype.utils.filemanip import split_filename as split_f

    report = Report()

    subj_path, basename, ext = split_f(fif_file)
    (data_path, sbj_name) = os.path.split(subj_path)
    print data_path

    # Read raw
    # If None the compensation in the data is not modified.
    # If set to n, e.g. 3, apply gradient compensation of grade n as for
    # CTF systems (compensation=3)
    raw = Raw(fif_file, preload=True)

    # select sensors
    select_sensors = mne.pick_types(raw.info, meg=True, ref_meg=False,
                                    exclude='bads')
    picks_meeg = mne.pick_types(raw.info, meg=True, eeg=True, exclude='bads')

    # save electrode locations
    sens_loc = [raw.info['chs'][i]['loc'][:3] for i in select_sensors]
    sens_loc = np.array(sens_loc)

    channel_coords_file = os.path.abspath("correct_channel_coords.txt")
    print '*** ' + channel_coords_file + '***'
    np.savetxt(channel_coords_file, sens_loc, fmt='%s')

    # save electrode names
    sens_names = np.array([raw.ch_names[pos] for pos in select_sensors],dtype = "str")

    # AP 21032016 
#    channel_names_file = os.path.join(data_path, "correct_channel_names.txt") 
    channel_names_file = os.path.abspath("correct_channel_names.txt")
    np.savetxt(channel_names_file,sens_names , fmt = '%s')
 
    ### filtering + downsampling
    raw.filter(l_freq=l_freq, h_freq=h_freq, picks=picks_meeg,
               method='iir', n_jobs=8)
#    raw.filter(l_freq = l_freq, h_freq = h_freq, picks = picks_meeg,
#               method='iir')
#    raw.resample(sfreq=down_sfreq, npad=0)

    ### 1) Fit ICA model using the FastICA algorithm
    # Other available choices are `infomax` or `extended-infomax`
    # We pass a float value between 0 and 1 to select n_components based on the
    # percentage of variance explained by the PCA components.
    ICA_title = 'Sources related to %s artifacts (red)'
    is_show = False # visualization
    reject = dict(mag=4e-12, grad=4000e-13)

    # check if we have an ICA, if yes, we load it
    ica_filename = os.path.join(subj_path,basename + "-ica.fif")  
    if os.path.exists(ica_filename) is False:
        ica = ICA(n_components=variance, method='fastica', max_iter=500) # , max_iter=500
        ica.fit(raw, picks=select_sensors, reject=reject) # decim = 3, 

        has_ICA = False
    else:
        has_ICA = True
        print ica_filename + '   exists!!!'
        ica = read_ica(ica_filename)
        ica.exclude = []

    # 2) identify bad components by analyzing latent sources.
    # generate ECG epochs use detection via phase statistics

    # if we just have exclude channels we jump these steps
#    if len(ica.exclude)==0:
    n_max_ecg = 3
    n_max_eog = 2

    # check if ECG_ch_name is in the raw channels
    if ECG_ch_name in raw.info['ch_names']:
        ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5,
                                       picks=select_sensors,
                                       ch_name=ECG_ch_name)
    # if not  a synthetic ECG channel is created from cross channel average
    else:
        ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5,
                                       picks=select_sensors)

    # ICA for ECG artifact
    # threshold=0.25 come default
    ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
    print scores
    print '\n len ecg_inds *** ' + str(len(ecg_inds)) + '***\n'
    if len(ecg_inds) > 0:
        ecg_evoked = ecg_epochs.average()

        fig1 = ica.plot_scores(scores, exclude=ecg_inds,
#.........这里部分代码省略.........

def preprocess_set_ICA_comp_fif_to_ts(fif_file, n_comp_exclude, l_freq, h_freq,
                                      down_sfreq, is_sensor_space):
    import os
    import numpy as np
    import sys

    import mne
    from mne.io import Raw
    from mne.preprocessing import read_ica
    from mne.report import Report

    from nipype.utils.filemanip import split_filename as split_f

    report = Report()

    subj_path, basename, ext = split_f(fif_file)
    (data_path,  sbj_name) = os.path.split(subj_path)

    print '*** SBJ %s' % sbj_name + '***'

#    n_session = int(filter(str.isdigit, basename))
#    print '*** n session = %d' % n_session + '***'

    # Read raw
    raw = Raw(fif_file, preload=True)

    # select sensors
    select_sensors = mne.pick_types(raw.info, meg=True, ref_meg=False,
                                    exclude='bads')
    picks_meeg = mne.pick_types(raw.info, meg=True, eeg=True,
                                exclude='bads')

    # save electrode locations
    sens_loc = [raw.info['chs'][i]['loc'][:3] for i in select_sensors]
    sens_loc = np.array(sens_loc)

    channel_coords_file = os.path.abspath("correct_channel_coords.txt")
    np.savetxt(channel_coords_file, sens_loc, fmt='%s')

    # save electrode names
    sens_names = np.array([raw.ch_names[pos] for pos in select_sensors],
                          dtype="str")

    channel_names_file = os.path.abspath("correct_channel_names.txt")
    np.savetxt(channel_names_file, sens_names, fmt='%s')

    # filtering + downsampling
    # TODO n_jobs=8
    raw.filter(l_freq=l_freq, h_freq=h_freq, picks=picks_meeg,
               method='iir',n_jobs=8)
#    raw.resample(sfreq=down_sfreq, npad=0)

    # load ICA
    is_show = False  # visualization
    ica_filename = os.path.join(subj_path, basename + '-ica.fif')
    if os.path.exists(ica_filename) is False:
        print "$$$ Warning, no %s found" % ica_filename
        sys.exit()
    else:
        ica = read_ica(ica_filename)

    # AP 210316
    '''
    print '*** ica.exclude before set components= ', ica.exclude
    if n_comp_exclude.has_key(sbj_name):
        print '*** ICA to be excluded for sbj %s ' % sbj_name + ' ' + str(n_comp_exclude[sbj_name]) + '***'
        matrix_c_ICA = n_comp_exclude[sbj_name]

        if not matrix_c_ICA[n_session-1]:
            print 'no ICA'
        else:
            print '*** ICA to be excluded for session %d ' %n_session + ' ' + str(matrix_c_ICA[n_session-1]) + '***'        
    ica.exclude = matrix_c_ICA[n_session-1]
    '''
    # AP new dict
    print '*** ica.exclude before set components= ', ica.exclude
    if n_comp_exclude.has_key(sbj_name):
        print '*** ICA to be excluded for sbj %s ' % sbj_name + ' ' + str(n_comp_exclude[sbj_name]) + '***'
        session_dict = n_comp_exclude[sbj_name]
        session_names = session_dict.keys()

        componentes = []
        for s in session_names:
            if basename.find(s) > -1:
                componentes = session_dict[s]
                break

        if len(componentes) == 0:
            print '\n no ICA to be excluded \n'
        else:
            print '\n *** ICA to be excluded for session %s ' % s + \
                    ' ' + str(componentes) + ' *** \n'

    ica.exclude = componentes

    print '\n *** ica.exclude after set components = ', ica.exclude

    fig1 = ica.plot_overlay(raw, show=is_show)
    report.add_figs_to_section(fig1, captions=['Signal'],
                               section='Signal quality')
#.........这里部分代码省略.........

    cond_names = ad.analysis_dict[subj][input_files].keys()
    # sort names so that VS comes before FFA!
    cond_names.sort(reverse=True)
    for cond in cond_names:
        if 'empty' not in cond:
            
            raw_path = ad._scratch_folder + '/' + input_files + '/' + subj
            in_fnames = ad.analysis_dict[subj][input_files][cond]['files'] 
            for fname in in_fnames:

                img_prefix = img_folder + '/' + cond

                print 'In: ', fname
                raw = Raw(fname, preload=True) # for finding events from raw, must be preloaded
                ica = read_ica(ica_folder + '/' + cond + '-ica.fif')
                # 2) identify bad components by analyzing latent sources.
                title = 'Sources related to %s artifacts (red)'

                # generate ECG epochs use detection via phase statistics

                picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True, ecg=True, stim=False, exclude='bads')

                # create_ecg_epochs is strange: it strips the channels of anything non M/EEG
                # UNLESS picks=None
                #picks=None
                # This will work with the above, but uses MASSIVE RAM
                # Not sure the ECG quality is good enough for the QRS-detector
                ecg_inds, scores = ica.find_bads_ecg(raw, method='ctps', ch_name='ECG002', threshold=0.25)
                if len(ecg_inds) < 1:
                    # destroy the ECG channel by changing it to an EMG!

raw = mne.io.read_raw_fif(run_fname, preload=True, add_eeg_ref=False)

###############################################################################
# We change the channel type for ECG and EOG.

raw.set_channel_types({'EEG061': 'eog', 'EEG062': 'eog', 'EEG063': 'ecg',
                       'EEG064': 'misc'})  # EEG064 free floating el.
raw.rename_channels({'EEG061': 'EOG061', 'EEG062': 'EOG062',
                     'EEG063': 'ECG063'})

###############################################################################
# Bad sensors are repaired.

raw.info['bads'] = bads
raw.interpolate_bads()
raw.set_eeg_reference()

###############################################################################
# Now let's get to some serious ICA preprocessing

ica_name = op.join(meg_dir, subject, 'run_%02d-ica.fif' % run)
ica = read_ica(ica_name)
n_max_ecg = 3  # use max 3 components
ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)
ecg_inds, scores_ecg = ica.find_bads_ecg(ecg_epochs, method='ctps',
                                         threshold=0.8)
ica.plot_sources(raw, exclude=ecg_inds)
ica.plot_scores(scores_ecg, exclude=ecg_inds)
ica.plot_properties(raw, ecg_inds)
ica.exclude += ecg_inds[:n_max_ecg]

def test_ica_additional():
    """Test additional functionality
    """
    stop2 = 500

    test_cov2 = deepcopy(test_cov)
    ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
              n_pca_components=4)
    ica.decompose_raw(raw, picks[:5])
    assert_true(ica.n_components_ < 5)

    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4)
    assert_raises(RuntimeError, ica.save, '')
    ica.decompose_raw(raw, picks=None, start=start, stop=stop2)

    # epochs extraction from raw fit
    assert_raises(RuntimeError, ica.get_sources_epochs, epochs)

    # test reading and writing
    test_ica_fname = op.join(op.dirname(tempdir), 'ica_test.fif')
    for cov in (None, test_cov):
        ica = ICA(noise_cov=cov, n_components=3, max_pca_components=4,
                  n_pca_components=4)
        ica.decompose_raw(raw, picks=picks, start=start, stop=stop2)
        sources = ica.get_sources_epochs(epochs)
        assert_true(sources.shape[1] == ica.n_components_)

        for exclude in [[], [0]]:
            ica.exclude = [0]
            ica.save(test_ica_fname)
            ica_read = read_ica(test_ica_fname)
            assert_true(ica.exclude == ica_read.exclude)
            # test pick merge -- add components
            ica.pick_sources_raw(raw, exclude=[1])
            assert_true(ica.exclude == [0, 1])
            #                 -- only as arg
            ica.exclude = []
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])
            #                 -- remove duplicates
            ica.exclude += [1]
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])

            ica_raw = ica.sources_as_raw(raw)
            assert_true(ica.exclude == [ica.ch_names.index(e) for e in
                                        ica_raw.info['bads']])

        ica.n_pca_components = 2
        ica.save(test_ica_fname)
        ica_read = read_ica(test_ica_fname)
        assert_true(ica.n_pca_components ==
                    ica_read.n_pca_components)
        ica.n_pca_components = 4
        ica_read.n_pca_components = 4

        ica.exclude = []
        ica.save(test_ica_fname)
        ica_read = read_ica(test_ica_fname)

        assert_true(ica.ch_names == ica_read.ch_names)

        assert_true(np.allclose(ica.mixing_matrix_, ica_read.mixing_matrix_,
                                rtol=1e-16, atol=1e-32))
        assert_array_equal(ica.pca_components_,
                           ica_read.pca_components_)
        assert_array_equal(ica.pca_mean_, ica_read.pca_mean_)
        assert_array_equal(ica.pca_explained_variance_,
                           ica_read.pca_explained_variance_)
        assert_array_equal(ica._pre_whitener, ica_read._pre_whitener)

        # assert_raises(RuntimeError, ica_read.decompose_raw, raw)
        sources = ica.get_sources_raw(raw)
        sources2 = ica_read.get_sources_raw(raw)
        assert_array_almost_equal(sources, sources2)

        _raw1 = ica.pick_sources_raw(raw, exclude=[1])
        _raw2 = ica_read.pick_sources_raw(raw, exclude=[1])
        assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])

    os.remove(test_ica_fname)
    # score funcs raw, with catch since "ties preclude exact" warning
    # XXX this should be fixed by a future PR...
    with warnings.catch_warnings(True) as w:
        sfunc_test = [ica.find_sources_raw(raw, target='EOG 061',
                score_func=n, start=0, stop=10)
                for n, f in score_funcs.items()]
    # score funcs raw

    # check lenght of scores
    [assert_true(ica.n_components_ == len(scores)) for scores in sfunc_test]

    # check univariate stats
    scores = ica.find_sources_raw(raw, score_func=stats.skew)
    # check exception handling
    assert_raises(ValueError, ica.find_sources_raw, raw,
                  target=np.arange(1))

    ## score funcs epochs ##
#.........这里部分代码省略.........

def test_ica_additional():
    """Test additional functionality
    """
    stop2 = 500

    test_cov2 = deepcopy(test_cov)
    ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
              n_pca_components=4)
    assert_true(ica.info is None)
    ica.decompose_raw(raw, picks[:5])
    assert_true(isinstance(ica.info, Info))
    assert_true(ica.n_components_ < 5)

    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4)
    assert_raises(RuntimeError, ica.save, '')
    ica.decompose_raw(raw, picks=None, start=start, stop=stop2)

    # epochs extraction from raw fit
    assert_raises(RuntimeError, ica.get_sources_epochs, epochs)

    # test reading and writing
    test_ica_fname = op.join(op.dirname(tempdir), 'ica_test.fif')
    for cov in (None, test_cov):
        ica = ICA(noise_cov=cov, n_components=3, max_pca_components=4,
                  n_pca_components=4)
        ica.decompose_raw(raw, picks=picks, start=start, stop=stop2)
        sources = ica.get_sources_epochs(epochs)
        assert_true(sources.shape[1] == ica.n_components_)

        for exclude in [[], [0]]:
            ica.exclude = [0]
            ica.save(test_ica_fname)
            ica_read = read_ica(test_ica_fname)
            assert_true(ica.exclude == ica_read.exclude)
            # test pick merge -- add components
            ica.pick_sources_raw(raw, exclude=[1])
            assert_true(ica.exclude == [0, 1])
            #                 -- only as arg
            ica.exclude = []
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])
            #                 -- remove duplicates
            ica.exclude += [1]
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])

            ica_raw = ica.sources_as_raw(raw)
            assert_true(ica.exclude == [ica_raw.ch_names.index(e) for e in
                                        ica_raw.info['bads']])

        ica.n_pca_components = 2
        ica.save(test_ica_fname)
        ica_read = read_ica(test_ica_fname)
        assert_true(ica.n_pca_components ==
                    ica_read.n_pca_components)
        ica.n_pca_components = 4
        ica_read.n_pca_components = 4

        ica.exclude = []
        ica.save(test_ica_fname)
        ica_read = read_ica(test_ica_fname)

        assert_true(ica.ch_names == ica_read.ch_names)
        assert_true(isinstance(ica_read.info, Info))  # XXX improve later
        assert_true(np.allclose(ica.mixing_matrix_, ica_read.mixing_matrix_,
                                rtol=1e-16, atol=1e-32))
        assert_array_equal(ica.pca_components_,
                           ica_read.pca_components_)
        assert_array_equal(ica.pca_mean_, ica_read.pca_mean_)
        assert_array_equal(ica.pca_explained_variance_,
                           ica_read.pca_explained_variance_)
        assert_array_equal(ica._pre_whitener, ica_read._pre_whitener)

        # assert_raises(RuntimeError, ica_read.decompose_raw, raw)
        sources = ica.get_sources_raw(raw)
        sources2 = ica_read.get_sources_raw(raw)
        assert_array_almost_equal(sources, sources2)

        _raw1 = ica.pick_sources_raw(raw, exclude=[1])
        _raw2 = ica_read.pick_sources_raw(raw, exclude=[1])
        assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])

    os.remove(test_ica_fname)
    # check scrore funcs
    for name, func in score_funcs.items():
        if name in score_funcs_unsuited:
            continue
        scores = ica.find_sources_raw(raw, target='EOG 061', score_func=func,
                                      start=0, stop=10)
        assert_true(ica.n_components_ == len(scores))

    # check univariate stats
    scores = ica.find_sources_raw(raw, score_func=stats.skew)
    # check exception handling
    assert_raises(ValueError, ica.find_sources_raw, raw,
                  target=np.arange(1))

    params = []
    params += [(None, -1, slice(2), [0, 1])]  # varicance, kurtosis idx params
#.........这里部分代码省略.........

                session_no = ''
                ica_check_eves = dict(FFA=['A','B'])
                ica_cond = 'FFA'

            ica_excludes = load_excludes(ica_excludes_path, subj, ica_cond)
            print 30*'*'
            print 'ICA excludes:', ica_excludes
            print 30*'*'
            raw_path = opj(ad._scratch_folder, input_files, subj)
            in_fnames = ad.analysis_dict[subj][input_files][cond]['files']
            events = mne.read_events(opj(eve_folder, cond + '-eve.fif'))
            eve_dict, id_dict = \
                 split_events_by_trialtype(events, condition=cond)
            for fname in in_fnames:

                ica = read_ica(opj(ica_folder, cond + '-ica.fif'))

                print 'In: ', fname
                raw = Raw(fname, preload=performBandpassFilter)
                rep_section_name = ''
                if performBandpassFilter:
                    raw.filter(filter_params['highpass'],
                               filter_params['lowpass'],
                               method='iir', n_jobs=1
                               )

                picks = mne.pick_types(raw.info, meg=True, eog=True)

                for trial_type in trial_types:
                    epochs = mne.Epochs(raw, eve_dict[trial_type],
                                    id_dict[trial_type],

def test_ica_additional():
    """Test additional ICA functionality
    """
    stop2 = 500
    raw = io.Raw(raw_fname, preload=True).crop(0, stop, False).crop(1.5)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    test_cov = read_cov(test_cov_name)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    # for testing eog functionality
    picks2 = pick_types(raw.info, meg=True, stim=False, ecg=False,
                        eog=True, exclude='bads')
    epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
                        baseline=(None, 0), preload=True)

    test_cov2 = deepcopy(test_cov)
    ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
              n_pca_components=4)
    assert_true(ica.info is None)
    ica.decompose_raw(raw, picks[:5])
    assert_true(isinstance(ica.info, Info))
    assert_true(ica.n_components_ < 5)

    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4)
    assert_raises(RuntimeError, ica.save, '')
    ica.decompose_raw(raw, picks=None, start=start, stop=stop2)

    # test warnings on bad filenames
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
        ica.save(ica_badname)
        read_ica(ica_badname)
    assert_true(len(w) == 2)

    # test decim
    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4)
    raw_ = raw.copy()
    for _ in range(3):
        raw_.append(raw_)
    n_samples = raw_._data.shape[1]
    ica.decompose_raw(raw, picks=None, decim=3)
    assert_true(raw_._data.shape[1], n_samples)

    # test expl var
    ica = ICA(n_components=1.0, max_pca_components=4,
              n_pca_components=4)
    ica.decompose_raw(raw, picks=None, decim=3)
    assert_true(ica.n_components_ == 4)

    # epochs extraction from raw fit
    assert_raises(RuntimeError, ica.get_sources_epochs, epochs)
    # test reading and writing
    test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
    for cov in (None, test_cov):
        ica = ICA(noise_cov=cov, n_components=2, max_pca_components=4,
                  n_pca_components=4)
        with warnings.catch_warnings(record=True):  # ICA does not converge
            ica.decompose_raw(raw, picks=picks, start=start, stop=stop2)
        sources = ica.get_sources_epochs(epochs)
        assert_true(ica.mixing_matrix_.shape == (2, 2))
        assert_true(ica.unmixing_matrix_.shape == (2, 2))
        assert_true(ica.pca_components_.shape == (4, len(picks)))
        assert_true(sources.shape[1] == ica.n_components_)

        for exclude in [[], [0]]:
            ica.exclude = [0]
            ica.save(test_ica_fname)
            ica_read = read_ica(test_ica_fname)
            assert_true(ica.exclude == ica_read.exclude)
            # test pick merge -- add components
            ica.pick_sources_raw(raw, exclude=[1])
            assert_true(ica.exclude == [0, 1])
            #                 -- only as arg
            ica.exclude = []
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])
            #                 -- remove duplicates
            ica.exclude += [1]
            ica.pick_sources_raw(raw, exclude=[0, 1])
            assert_true(ica.exclude == [0, 1])

            # test basic include
            ica.exclude = []
            ica.pick_sources_raw(raw, include=[1])

            ica_raw = ica.sources_as_raw(raw)
            assert_true(ica.exclude == [ica_raw.ch_names.index(e) for e in
                                        ica_raw.info['bads']])

        # test filtering
        d1 = ica_raw._data[0].copy()
        with warnings.catch_warnings(record=True):  # dB warning
            ica_raw.filter(4, 20)
#.........这里部分代码省略.........