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# Author: Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)
import warnings
import os.path as op
import numpy as np
from nose.tools import assert_true, assert_raises
from numpy.testing import assert_array_equal
from mne import io, read_events, Epochs, pick_types
from mne.decoding.classifier import Scaler, FilterEstimator
from mne.decoding.classifier import PSDEstimator, ConcatenateChannels
warnings.simplefilter('always') # enable b/c these tests throw warnings
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
start, stop = 0, 8
data_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
raw_fname = op.join(data_dir, 'test_raw.fif')
event_name = op.join(data_dir, 'test-eve.fif')
def test_scaler():
"""Test methods of Scaler
"""
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()
scaler = Scaler(epochs.info)
y = epochs.events[:, -1]
# np invalid divide value warnings
with warnings.catch_warnings(record=True):
X = scaler.fit_transform(epochs_data, y)
assert_true(X.shape == epochs_data.shape)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X2, X)
# Test init exception
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs, y)
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()
filt = FilterEstimator(epochs.info, 1, 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)
# Test init exception
assert_raises(ValueError, filt.fit, epochs, y)
assert_raises(ValueError, filt.transform, epochs, y)
def test_psdestimator():
"""Test methods of PSDEstimator
"""
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()
psd = PSDEstimator(2 * np.pi, 0, np.inf)
y = epochs.events[:, -1]
X = psd.fit_transform(epochs_data, y)
assert_true(X.shape[0] == epochs_data.shape[0])
assert_array_equal(psd.fit(epochs_data, y).transform(epochs_data), X)
# Test init exception
assert_raises(ValueError, psd.fit, epochs, y)
assert_raises(ValueError, psd.transform, epochs, y)
def test_concatenatechannels():
"""Test methods of ConcatenateChannels
"""
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]
with warnings.catch_warnings(record=True) as w:
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs_data = epochs.get_data()
concat = ConcatenateChannels(epochs.info)
y = epochs.events[:, -1]
X = concat.fit_transform(epochs_data, y)
# Check data dimensions
assert_true(X.shape[0] == epochs_data.shape[0])
assert_true(X.shape[1] == epochs_data.shape[1] * epochs_data.shape[2])
assert_array_equal(concat.fit(epochs_data, y).transform(epochs_data), X)
# Check if data is preserved
n_times = epochs_data.shape[2]
assert_array_equal(epochs_data[0, 0, 0:n_times], X[0, 0:n_times])
# Test init exception
assert_raises(ValueError, concat.fit, epochs, y)
assert_raises(ValueError, concat.transform, epochs, y)
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