def spatio_temporal_cluster_test_connectivity():
"""Test cluster level permutations with and without connectivity """
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
rng = np.random.RandomState(0)
noise1_2d = rng.randn(condition1_2d.shape[0], condition1_2d.shape[1], 10)
data1_2d = np.transpose(np.dstack((condition1_2d, noise1_2d)), [0, 2, 1])
noise2_d2 = rng.randn(condition2_2d.shape[0], condition2_2d.shape[1], 10)
data2_2d = np.transpose(np.dstack((condition2_2d, noise2_d2)), [0, 2, 1])
conn = grid_to_graph(data1_2d.shape[-1], 1)
threshold = dict(start=4.0, step=2)
T_obs, clusters, p_values_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d], connectivity=conn,
n_permutations=50, tail=1, seed=1,
threshold=threshold)
T_obs, clusters, p_values_no_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d],
n_permutations=50, tail=1, seed=1,
threshold=threshold)
assert_equal(np.sum(p_values_conn < 0.05), np.sum(p_values_no_conn < 0.05))
def test_spatio_temporal_cluster_connectivity():
"""Test spatio-temporal cluster permutations."""
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
condition1_1d, condition2_1d, condition1_2d, condition2_2d = \
_get_conditions()
rng = np.random.RandomState(0)
noise1_2d = rng.randn(condition1_2d.shape[0], condition1_2d.shape[1], 10)
data1_2d = np.transpose(np.dstack((condition1_2d, noise1_2d)), [0, 2, 1])
noise2_d2 = rng.randn(condition2_2d.shape[0], condition2_2d.shape[1], 10)
data2_2d = np.transpose(np.dstack((condition2_2d, noise2_d2)), [0, 2, 1])
conn = grid_to_graph(data1_2d.shape[-1], 1)
threshold = dict(start=4.0, step=2)
T_obs, clusters, p_values_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d], connectivity=conn,
n_permutations=50, tail=1, seed=1,
threshold=threshold, buffer_size=None)
buffer_size = data1_2d.size // 10
T_obs, clusters, p_values_no_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d],
n_permutations=50, tail=1, seed=1,
threshold=threshold, n_jobs=2,
buffer_size=buffer_size)
assert_equal(np.sum(p_values_conn < 0.05), np.sum(p_values_no_conn < 0.05))
# make sure results are the same without buffer_size
T_obs, clusters, p_values2, hist2 = \
spatio_temporal_cluster_test([data1_2d, data2_2d],
n_permutations=50, tail=1, seed=1,
threshold=threshold, n_jobs=2,
buffer_size=None)
assert_array_equal(p_values_no_conn, p_values2)
assert_raises(ValueError,
spatio_temporal_cluster_test, [data1_2d, data2_2d],
tail=1,
threshold=-2.)
assert_raises(ValueError,
spatio_temporal_cluster_test, [data1_2d, data2_2d],
tail=-1,
threshold=2.)
assert_raises(ValueError,
spatio_temporal_cluster_test, [data1_2d, data2_2d],
tail=0,
threshold=-1)
def test_spatio_temporal_cluster_connectivity():
"""Test spatio-temporal cluster permutations."""
try:
try:
from sklearn.feature_extraction.image import grid_to_graph
except ImportError:
from scikits.learn.feature_extraction.image import grid_to_graph
except ImportError:
return
condition1_1d, condition2_1d, condition1_2d, condition2_2d = \
_get_conditions()
rng = np.random.RandomState(0)
noise1_2d = rng.randn(condition1_2d.shape[0], condition1_2d.shape[1], 10)
data1_2d = np.transpose(np.dstack((condition1_2d, noise1_2d)), [0, 2, 1])
noise2_d2 = rng.randn(condition2_2d.shape[0], condition2_2d.shape[1], 10)
data2_2d = np.transpose(np.dstack((condition2_2d, noise2_d2)), [0, 2, 1])
conn = grid_to_graph(data1_2d.shape[-1], 1)
threshold = dict(start=4.0, step=2)
T_obs, clusters, p_values_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d], connectivity=conn,
n_permutations=50, tail=1, seed=1,
threshold=threshold, buffer_size=None)
buffer_size = data1_2d.size // 10
T_obs, clusters, p_values_no_conn, hist = \
spatio_temporal_cluster_test([data1_2d, data2_2d],
n_permutations=50, tail=1, seed=1,
threshold=threshold, n_jobs=2,
buffer_size=buffer_size)
assert_equal(np.sum(p_values_conn < 0.05), np.sum(p_values_no_conn < 0.05))
# make sure results are the same without buffer_size
T_obs, clusters, p_values2, hist2 = \
spatio_temporal_cluster_test([data1_2d, data2_2d],
n_permutations=50, tail=1, seed=1,
threshold=threshold, n_jobs=2,
buffer_size=None)
assert_array_equal(p_values_no_conn, p_values2)
assert_raises(ValueError, spatio_temporal_cluster_test,
[data1_2d, data2_2d], tail=1, threshold=-2.)
assert_raises(ValueError, spatio_temporal_cluster_test,
[data1_2d, data2_2d], tail=-1, threshold=2.)
assert_raises(ValueError, spatio_temporal_cluster_test,
[data1_2d, data2_2d], tail=0, threshold=-1)
