def test_filterestimator():
"""Test methods of FilterEstimator
"""
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False, eog=False, exclude="bads")
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
# Add tests for different combinations of l_freq and h_freq
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=40)
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
assert_array_equal(filt.fit(epochs_data, y).transform(epochs_data), X)
filt = FilterEstimator(epochs.info, l_freq=0, h_freq=40)
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=1)
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
assert_raises(ValueError, filt.fit_transform, epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=None)
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
# Test init exception
assert_raises(ValueError, filt.fit, epochs, y)
assert_raises(ValueError, filt.transform, epochs, y)
def test_filterestimator():
"""Test methods of FilterEstimator."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
# Add tests for different combinations of l_freq and h_freq
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=80)
y = epochs.events[:, -1]
X = filt.fit_transform(epochs_data, y)
assert (X.shape == epochs_data.shape)
assert_array_equal(filt.fit(epochs_data, y).transform(epochs_data), X)
filt = FilterEstimator(epochs.info, l_freq=None, h_freq=40,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
y = epochs.events[:, -1]
X = filt.fit_transform(epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=1)
y = epochs.events[:, -1]
with pytest.warns(RuntimeWarning, match='longer than the signal'):
pytest.raises(ValueError, filt.fit_transform, epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=None,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
X = filt.fit_transform(epochs_data, y)
# Test init exception
pytest.raises(ValueError, filt.fit, epochs, y)
pytest.raises(ValueError, filt.transform, epochs)
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=90, buffer_size=1000)
# Decoding in sensor space using a linear SVM
n_times = len(rt_epochs.times)
from sklearn import preprocessing # noqa
from sklearn.svm import SVC # noqa
from sklearn.pipeline import Pipeline # noqa
from sklearn.model_selection import cross_val_score, ShuffleSplit # noqa
from mne.decoding import Vectorizer, FilterEstimator # noqa
scores_x, scores, std_scores = [], [], []
# don't highpass filter because it's epoched data and the signal length
# is small
filt = FilterEstimator(rt_epochs.info, None, 40, fir_design='firwin')
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info,
meg='grad',
eeg=False,
eog=True,
stim=False,
exclude=raw.info['bads'])
ax = plt.subplot(111)
ax.set_xlabel('Trials')
def test_filterestimator():
"""Test methods of FilterEstimator."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
# Add tests for different combinations of l_freq and h_freq
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=80,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
assert_array_equal(filt.fit(epochs_data, y).transform(epochs_data), X)
filt = FilterEstimator(epochs.info, l_freq=None, h_freq=40,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=1, h_freq=1)
y = epochs.events[:, -1]
with warnings.catch_warnings(record=True): # stop freq attenuation warning
assert_raises(ValueError, filt.fit_transform, epochs_data, y)
filt = FilterEstimator(epochs.info, l_freq=40, h_freq=None,
filter_length='auto',
l_trans_bandwidth='auto', h_trans_bandwidth='auto')
with warnings.catch_warnings(record=True): # stop freq attenuation warning
X = filt.fit_transform(epochs_data, y)
# Test init exception
assert_raises(ValueError, filt.fit, epochs, y)
assert_raises(ValueError, filt.transform, epochs, y)
# send raw buffers
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=90, buffer_size=1000)
# Decoding in sensor space using a linear SVM
n_times = len(rt_epochs.times)
from sklearn import preprocessing # noqa
from sklearn.svm import SVC # noqa
from sklearn.pipeline import Pipeline # noqa
from sklearn.cross_validation import cross_val_score, ShuffleSplit # noqa
from mne.decoding import Vectorizer, FilterEstimator # noqa
scores_x, scores, std_scores = [], [], []
filt = FilterEstimator(rt_epochs.info, 1, 40)
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info,
meg='grad',
eeg=False,
eog=True,
stim=False,
exclude=raw.info['bads'])
for ev_num, ev in enumerate(rt_epochs.iter_evoked()):
# The with statement is necessary to ensure a clean exit
with StimServer('localhost', port=4218) as stim_server:
# The channels to be used while decoding
picks = mne.pick_types(raw.info,
meg='grad',
eeg=False,
eog=True,
stim=True,
exclude=raw.info['bads'])
rt_client = MockRtClient(raw)
# Constructing the pipeline for classification
filt = FilterEstimator(raw.info, 1, 40)
scaler = preprocessing.StandardScaler()
concatenator = ConcatenateChannels()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('concat', concatenator),
('scaler', scaler), ('svm', clf)])
stim_server.start(verbose=True)
# Just some initially decided events to be simulated
# Rest will decided on the fly
ev_list = [4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4]
score_c1, score_c2, score_x = [], [], []
# The channels to be used while decoding
picks = mne.pick_types(raw.info,
meg='grad',
eeg=False,
eog=False,
stim=False,
exclude=raw.info['bads'])
rt_client = MockRtClient(raw)
# Constructing the pipeline for classification
# don't highpass filter because of short signal length of epochs
filt = FilterEstimator(
raw.info,
None,
40,
# keep all channels that are picked from the
# RtClient
picks=np.arange(len(picks), dtype=int))
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
stim_server.start(verbose=True)
# Just some initially decided events to be simulated
# Rest will decided on the fly
ev_list = [4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4]
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=90, buffer_size=1000)
# Decoding in sensor space using a linear SVM
n_times = len(rt_epochs.times)
from sklearn import preprocessing # noqa
from sklearn.svm import SVC # noqa
from sklearn.pipeline import Pipeline # noqa
from sklearn.cross_validation import cross_val_score, ShuffleSplit # noqa
from mne.decoding import Vectorizer, FilterEstimator # noqa
scores_x, scores, std_scores = [], [], []
# don't highpass filter because it's epoched data and the signal length
# is small
filt = FilterEstimator(rt_epochs.info, None, 40)
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info,
meg='grad',
eeg=False,
eog=True,
stim=False,
exclude=raw.info['bads'])
ax = plt.subplot(111)
ax.set_xlabel('Trials')
