def test_build_parcellations(random_state=0):
"""Test parcellations building.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
mask = np.ones(shape, dtype=bool)
# data generation
data1 = np.ones(shape)
data1[1:3, 1:3, 1:3] = 2.
data2 = np.ones(shape)
data2[3:, 3:, 3:] = 4.
data = np.ones((4, np.prod(shape))) # 4 ravelized images
data[0] = np.ravel(data1)
data[1] = np.ravel(data2)
# Test _build_parcellations function
parcelled_data, labels = _build_parcellations(
data,
mask,
n_parcellations=2,
n_parcels=3,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=8,
random_state=rng)
# check parcelled_data
assert_equal(parcelled_data.shape, (4, 3 * 2))
assert_array_equal(
np.sort(np.unique(parcelled_data[0])), # order is hard to predict
[1, 2])
assert_array_equal(
np.sort(np.unique(parcelled_data[1])), # order is hard to predict
[1, 4])
assert_array_equal(parcelled_data[2], [1, 1, 1, 1, 1, 1])
assert_array_equal(parcelled_data[3], [1, 1, 1, 1, 1, 1])
# check labels
assert_equal(len(labels.shape), 1)
assert_array_equal(np.unique(labels), np.arange(2 * 3))
def test_build_parcellations(random_state=0):
"""Test parcellations building.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
mask = np.ones(shape, dtype=bool)
# data generation
data1 = np.ones(shape)
data1[1:3, 1:3, 1:3] = 2.
data2 = np.ones(shape)
data2[3:, 3:, 3:] = 4.
data = np.ones((4, np.prod(shape))) # 4 ravelized images
data[0] = np.ravel(data1)
data[1] = np.ravel(data2)
# Test _build_parcellations function
parcelled_data, labels = _build_parcellations(
data, mask, n_parcellations=2, n_parcels=3,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=8, random_state=rng)
# check parcelled_data
assert_equal(parcelled_data.shape, (4, 3 * 2))
assert_array_equal(
np.sort(np.unique(parcelled_data[0])), # order is hard to predict
[1, 2])
assert_array_equal(
np.sort(np.unique(parcelled_data[1])), # order is hard to predict
[1, 4])
assert_array_equal(parcelled_data[2], [1, 1, 1, 1, 1, 1])
assert_array_equal(parcelled_data[3], [1, 1, 1, 1, 1, 1])
# check labels
assert_equal(len(labels.shape), 1)
assert_array_equal(np.unique(labels), np.arange(2 * 3))
def test_rpbi_core_withcovars(random_state=0):
"""Test Randomized Parcellation Based Inference core function with covars.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
n_voxels = np.prod(shape)
mask = np.ones(shape, dtype=bool)
# data generation
data = np.zeros(shape)
data[1:3, 1:3, 1:3] = 2.
data = data.reshape((1, -1))
data = np.repeat(data, 8, 0)
# add noise to avoid constant columns
data += 0.1 * rng.randn(data.shape[0], data.shape[1])
# Parcellate data and extract signal averages
n_parcellations = 2
n_parcels = 3
parcelled_data, labels = _build_parcellations(
data, mask, n_parcellations=n_parcellations, n_parcels=n_parcels,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=6, random_state=rng)
# Covariates (dummy)
covars = 0.1 * rng.randn(8).reshape((-1, 1))
# RPBI from already parcelled data
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
np.ones((8, 1)), parcelled_data, n_parcellations, labels,
n_parcels, confounding_vars=covars, model_intercept=False,
threshold=0.05 / n_parcels, n_perm=9, random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels,))
assert_array_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels,))
assert_array_equal(counting_statistic_original_data, 2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9,))
assert_array_equal(h0, np.zeros(9))
# Same thing with model_intercept=True
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
np.ones((8, 1)), parcelled_data, n_parcellations, labels,
n_parcels, confounding_vars=covars, model_intercept=True,
threshold=0.05 / n_parcels, n_perm=9, random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels,))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels,))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9,))
assert_array_almost_equal(h0, np.zeros(9))
# Replace intercept test with a more complex test
rng = check_random_state(random_state)
tested_var = np.ones(8)
tested_var[0:4] = 0
parcelled_data[0:4] *= -1
pvalues, counting_statistic_original_data, h0 = rpbi_core(
tested_var, parcelled_data, n_parcellations, labels,
n_parcels, confounding_vars=covars, model_intercept=False,
threshold=0.1 / n_parcels, n_perm=9, random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels,))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels,))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9,))
assert_array_almost_equal(h0, np.zeros(9))
# Same thing with intercept modelling
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
tested_var, parcelled_data, n_parcellations, labels,
n_parcels, confounding_vars=covars, model_intercept=True,
threshold=0.1 / n_parcels, n_perm=9, random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels,))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels,))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9,))
assert_array_almost_equal(h0, np.zeros(9))
def test_compute_counting_statistic_from_parcel_level_scores(random_state=1):
"""Test the computation of RPBI's counting statistic.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
n_voxels = np.prod(shape)
mask = np.ones(shape, dtype=bool)
# data generation
data1 = np.ones(shape)
data1[1:3, 1:3, 1:3] = 2.
data2 = np.ones(shape)
data2[3:, 3:, 3:] = 4.
data = np.ones((4, np.prod(shape))) # 4 ravelized images
data[0] = np.ravel(data1)
data[1] = np.ravel(data2)
# Parcellate data and extract signal averages
n_parcellations = 2
n_parcels = 3
parcelled_data, labels = _build_parcellations(
data, mask, n_parcellations=n_parcellations, n_parcels=n_parcels,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=6, random_state=rng)
parcel_level_results = GrowableSparseArray(n_rows=2)
data_tmp = parcelled_data[0]
data_tmp[data_tmp < 2] = 0
parcel_level_results.append(0, data_tmp)
data_tmp = parcelled_data[1]
data_tmp[data_tmp < 2] = 0
parcel_level_results.append(1, data_tmp)
parcellation_masks = np.zeros((n_parcellations * n_parcels, n_voxels))
for j in np.arange(n_parcellations): # loop on parcellations
label_slice = slice(j * n_voxels, (j + 1) * n_voxels)
for l in np.unique(labels[label_slice]):
parcellation_masks[l] = labels[label_slice] == l
parcellation_masks = sparse.coo_matrix(
parcellation_masks.astype(np.float32)).tocsr()
# Transform back data
# (transformed data should be similar to the original data (up to
# thresholding and sum across parcellations) since by construction
# the signal is homogeneous within each parcel for each subject)
thresholded_data = data.copy()
thresholded_data[thresholded_data < 2] = 0.
thresholded_data *= 2.
res = _compute_counting_statistic_from_parcel_level_scores(
parcel_level_results.get_data(), slice(0, 2), parcellation_masks,
n_parcellations, n_parcellations * n_parcels)
counting_stats_original_data, h0 = res
assert_array_equal(counting_stats_original_data,
thresholded_data[0])
assert_array_equal(h0, [8])
# Same thing but only for the permuted data
res = _compute_counting_statistic_from_parcel_level_scores(
parcel_level_results.get_data()[2:], slice(1, 2),
parcellation_masks, n_parcellations, n_parcellations * n_parcels)
counting_stats_original_data, h0 = res
assert_array_equal(counting_stats_original_data, [])
assert_array_equal(h0, [8])
def test_rpbi_core_withcovars(random_state=0):
"""Test Randomized Parcellation Based Inference core function with covars.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
n_voxels = np.prod(shape)
mask = np.ones(shape, dtype=bool)
# data generation
data = np.zeros(shape)
data[1:3, 1:3, 1:3] = 2.
data = data.reshape((1, -1))
data = np.repeat(data, 8, 0)
# add noise to avoid constant columns
data += 0.1 * rng.randn(data.shape[0], data.shape[1])
# Parcellate data and extract signal averages
n_parcellations = 2
n_parcels = 3
parcelled_data, labels = _build_parcellations(
data,
mask,
n_parcellations=n_parcellations,
n_parcels=n_parcels,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=6,
random_state=rng)
# Covariates (dummy)
covars = 0.1 * rng.randn(8).reshape((-1, 1))
# RPBI from already parcelled data
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
np.ones((8, 1)),
parcelled_data,
n_parcellations,
labels,
n_parcels,
confounding_vars=covars,
model_intercept=False,
threshold=0.05 / n_parcels,
n_perm=9,
random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels, ))
assert_array_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels, ))
assert_array_equal(counting_statistic_original_data, 2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9, ))
assert_array_equal(h0, np.zeros(9))
# Same thing with model_intercept=True
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
np.ones((8, 1)),
parcelled_data,
n_parcellations,
labels,
n_parcels,
confounding_vars=covars,
model_intercept=True,
threshold=0.05 / n_parcels,
n_perm=9,
random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels, ))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels, ))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9, ))
assert_array_almost_equal(h0, np.zeros(9))
# Replace intercept test with a more complex test
rng = check_random_state(random_state)
tested_var = np.ones(8)
tested_var[0:4] = 0
parcelled_data[0:4] *= -1
pvalues, counting_statistic_original_data, h0 = rpbi_core(
tested_var,
parcelled_data,
n_parcellations,
labels,
n_parcels,
confounding_vars=covars,
model_intercept=False,
threshold=0.1 / n_parcels,
n_perm=9,
random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels, ))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels, ))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9, ))
assert_array_almost_equal(h0, np.zeros(9))
# Same thing with intercept modelling
rng = check_random_state(random_state)
pvalues, counting_statistic_original_data, h0 = rpbi_core(
tested_var,
parcelled_data,
n_parcellations,
labels,
n_parcels,
confounding_vars=covars,
model_intercept=True,
threshold=0.1 / n_parcels,
n_perm=9,
random_state=rng)
# check pvalues
expected_pvalues = np.zeros(shape)
expected_pvalues[1:3, 1:3, 1:3] = 1.
expected_pvalues = np.ravel(expected_pvalues)
assert_equal(pvalues.shape, (n_voxels, ))
assert_array_almost_equal(pvalues, expected_pvalues)
# check counting statistic
assert_equal(counting_statistic_original_data.shape, (n_voxels, ))
assert_array_almost_equal(counting_statistic_original_data,
2 * expected_pvalues)
# h0
assert_equal(h0.shape, (9, ))
assert_array_almost_equal(h0, np.zeros(9))
def test_compute_counting_statistic_from_parcel_level_scores(random_state=1):
"""Test the computation of RPBI's counting statistic.
"""
# check random state
rng = check_random_state(random_state)
# Generate toy data
# define data structure
shape = (5, 5, 5)
n_voxels = np.prod(shape)
mask = np.ones(shape, dtype=bool)
# data generation
data1 = np.ones(shape)
data1[1:3, 1:3, 1:3] = 2.
data2 = np.ones(shape)
data2[3:, 3:, 3:] = 4.
data = np.ones((4, np.prod(shape))) # 4 ravelized images
data[0] = np.ravel(data1)
data[1] = np.ravel(data2)
# Parcellate data and extract signal averages
n_parcellations = 2
n_parcels = 3
parcelled_data, labels = _build_parcellations(
data,
mask,
n_parcellations=n_parcellations,
n_parcels=n_parcels,
# make sure we use observations 1 and 2 at least once
n_bootstrap_samples=6,
random_state=rng)
parcel_level_results = GrowableSparseArray(n_rows=2)
data_tmp = parcelled_data[0]
data_tmp[data_tmp < 2] = 0
parcel_level_results.append(0, data_tmp)
data_tmp = parcelled_data[1]
data_tmp[data_tmp < 2] = 0
parcel_level_results.append(1, data_tmp)
parcellation_masks = np.zeros((n_parcellations * n_parcels, n_voxels))
for j in np.arange(n_parcellations): # loop on parcellations
label_slice = slice(j * n_voxels, (j + 1) * n_voxels)
for l in np.unique(labels[label_slice]):
parcellation_masks[l] = labels[label_slice] == l
parcellation_masks = sparse.coo_matrix(
parcellation_masks.astype(np.float32)).tocsr()
# Transform back data
# (transformed data should be similar to the original data (up to
# thresholding and sum across parcellations) since by construction
# the signal is homogeneous within each parcel for each subject)
thresholded_data = data.copy()
thresholded_data[thresholded_data < 2] = 0.
thresholded_data *= 2.
res = _compute_counting_statistic_from_parcel_level_scores(
parcel_level_results.get_data(), slice(0, 2), parcellation_masks,
n_parcellations, n_parcellations * n_parcels)
counting_stats_original_data, h0 = res
assert_array_equal(counting_stats_original_data, thresholded_data[0])
assert_array_equal(h0, [8])
# Same thing but only for the permuted data
res = _compute_counting_statistic_from_parcel_level_scores(
parcel_level_results.get_data()[2:], slice(1, 2), parcellation_masks,
n_parcellations, n_parcellations * n_parcels)
counting_stats_original_data, h0 = res
assert_array_equal(counting_stats_original_data, [])
assert_array_equal(h0, [8])
