def test_random_permutation():
"""Test random permutation function."""
n_samples = 10
random_state = 42
python_randperm = random_permutation(n_samples, random_state)
# matlab output when we execute rng(42), randperm(10)
matlab_randperm = np.array([7, 6, 5, 1, 4, 9, 10, 3, 8, 2])
assert_array_equal(python_randperm, matlab_randperm - 1)
def test_random_permutation():
"""Test random permutation function."""
n_samples = 10
random_state = 42
python_randperm = random_permutation(n_samples, random_state)
# matlab output when we execute rng(42), randperm(10)
matlab_randperm = np.array([7, 6, 5, 1, 4, 9, 10, 3, 8, 2])
assert_array_equal(python_randperm, matlab_randperm - 1)
def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state):
"""Generate data."""
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
raw = read_raw_fif(raw_fname, preload=True)
if ch_type == 'eeg':
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
else:
picks = pick_types(raw.info, meg=ch_type, eeg=False, exclude='bads')
# select a small number of channels for the test
number_of_channels_to_use = 5
idx_perm = random_permutation(picks.shape[0], random_state)
picks = picks[idx_perm[:number_of_channels_to_use]]
raw.filter(1,
45,
picks=picks,
filter_length='10s',
l_trans_bandwidth=0.5,
h_trans_bandwidth=0.5,
phase='zero-double',
fir_window='hann') # use the old way
X = raw[picks, :][0][:, ::20]
# Subtract the mean
mean_X = X.mean(axis=1)
X -= mean_X[:, None]
# pre_whitening: z-score
X /= np.std(X)
T = X.shape[1]
cov_X = np.dot(X, X.T) / T
# Let's whiten the data
U, D, _ = svd(cov_X)
W = np.dot(U, U.T / np.sqrt(D)[:, None])
Y = np.dot(W, X)
return Y
def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state):
"""Generate data."""
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
raw = read_raw_fif(raw_fname, preload=True, add_eeg_ref=False)
if ch_type == 'eeg':
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
else:
picks = pick_types(raw.info, meg=ch_type,
eeg=False, exclude='bads')
# select a small number of channels for the test
number_of_channels_to_use = 5
idx_perm = random_permutation(picks.shape[0], random_state)
picks = picks[idx_perm[:number_of_channels_to_use]]
with warnings.catch_warnings(record=True): # deprecated params
raw.filter(1, 45, picks=picks)
# Eventually we will need to add these, but for now having none of
# them is a nice deprecation sanity check.
# filter_length='10s',
# l_trans_bandwidth=0.5, h_trans_bandwidth=0.5,
# phase='zero-double', fir_window='hann') # use the old way
X = raw[picks, :][0][:, ::20]
# Subtract the mean
mean_X = X.mean(axis=1)
X -= mean_X[:, None]
# pre_whitening: z-score
X /= np.std(X)
T = X.shape[1]
cov_X = np.dot(X, X.T) / T
# Let's whiten the data
U, D, _ = svd(cov_X)
W = np.dot(U, U.T / np.sqrt(D)[:, None])
Y = np.dot(W, X)
return Y
def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state):
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
raw = Raw(raw_fname, preload=True)
if ch_type == 'eeg':
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
else:
picks = pick_types(raw.info, meg=ch_type, eeg=False, exclude='bads')
# select a small number of channels for the test
number_of_channels_to_use = 5
idx_perm = random_permutation(picks.shape[0], random_state)
picks = picks[idx_perm[:number_of_channels_to_use]]
with warnings.catch_warnings(record=True): # deprecated params
raw.filter(1, 45, picks=picks)
# Eventually we will need to add these, but for now having none of
# them is a nice deprecation sanity check.
# filter_length='10s',
# l_trans_bandwidth=0.5, h_trans_bandwidth=0.5,
# phase='zero-double') # use the old way
X = raw[picks, :][0][:, ::20]
# Subtract the mean
mean_X = X.mean(axis=1)
X -= mean_X[:, None]
# pre_whitening: z-score
X /= np.std(X)
T = X.shape[1]
cov_X = np.dot(X, X.T) / T
# Let's whiten the data
U, D, _ = svd(cov_X)
W = np.dot(U, U.T / np.sqrt(D)[:, None])
Y = np.dot(W, X)
return Y
def generate_data_for_comparing_against_eeglab_infomax(ch_type, random_state):
data_dir = op.join(testing.data_path(download=False), 'MEG', 'sample')
raw_fname = op.join(data_dir, 'sample_audvis_trunc_raw.fif')
raw = Raw(raw_fname, preload=True)
if ch_type == 'eeg':
picks = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
else:
picks = pick_types(raw.info, meg=ch_type,
eeg=False, exclude='bads')
# select a small number of channels for the test
number_of_channels_to_use = 5
idx_perm = random_permutation(picks.shape[0], random_state)
picks = picks[idx_perm[:number_of_channels_to_use]]
raw.filter(1, 45, n_jobs=2)
X = raw[picks, :][0][:, ::20]
# Substract the mean
mean_X = X.mean(axis=1)
X -= mean_X[:, None]
# pre_whitening: z-score
X /= np.std(X)
T = X.shape[1]
cov_X = np.dot(X, X.T) / T
# Let's whiten the data
U, D, _ = svd(cov_X)
W = np.dot(U, U.T / np.sqrt(D)[:, None])
Y = np.dot(W, X)
return Y
