def test_gsarray_append_data():
"""This function tests GrowableSparseArray creation and filling.
"""
# Simplest example
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.ones(5))
assert_array_equal(gs_array.get_data()['row'], np.zeros(5))
assert_array_equal(gs_array.get_data()['col'], np.arange(5))
assert_array_equal(gs_array.get_data()['data'], np.ones(5))
# Append with no structure extension needed
gs_array = GrowableSparseArray(n_rows=1, max_elts=10)
gs_array.append(0, np.ones(5))
assert_array_equal(gs_array.get_data()['row'], np.zeros(5))
assert_array_equal(gs_array.get_data()['col'], np.arange(5))
assert_array_equal(gs_array.get_data()['data'], np.ones(5))
# Void array
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.zeros(5))
assert_array_equal(gs_array.get_data()['row'], [])
assert_array_equal(gs_array.get_data()['col'], [])
assert_array_equal(gs_array.get_data()['data'], [])
# Toy example
gs_array = GrowableSparseArray(n_rows=10)
for i in range(10):
data = np.arange(10) - i
data[data < 8] = 0
gs_array.append(i, data)
assert_array_equal(gs_array.get_data()['row'], np.array([0., 0., 1.]))
assert_array_equal(gs_array.get_data()['col'], [8, 9, 9])
assert_array_equal(gs_array.get_data()['data'], [8., 9., 8.])
def test_gsarray_append_data():
"""This function tests GrowableSparseArray creation and filling.
"""
# Simplest example
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.ones(5))
assert_array_equal(gs_array.get_data()['row'], np.zeros(5))
assert_array_equal(gs_array.get_data()['col'], np.arange(5))
assert_array_equal(gs_array.get_data()['data'], np.ones(5))
# Append with no structure extension needed
gs_array = GrowableSparseArray(n_rows=1, max_elts=10)
gs_array.append(0, np.ones(5))
assert_array_equal(gs_array.get_data()['row'], np.zeros(5))
assert_array_equal(gs_array.get_data()['col'], np.arange(5))
assert_array_equal(gs_array.get_data()['data'], np.ones(5))
# Void array
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.zeros(5))
assert_array_equal(gs_array.get_data()['row'], [])
assert_array_equal(gs_array.get_data()['col'], [])
assert_array_equal(gs_array.get_data()['data'], [])
# Toy example
gs_array = GrowableSparseArray(n_rows=10)
for i in range(10):
data = np.arange(10) - i
data[data < 8] = 0
gs_array.append(i, data)
assert_array_equal(gs_array.get_data()['row'], np.array([0., 0., 1.]))
assert_array_equal(gs_array.get_data()['col'], [8, 9, 9])
assert_array_equal(gs_array.get_data()['data'], [8., 9., 8.])
def test_gsarray_merge():
"""This function tests GrowableSparseArray merging.
Because of the specific usage of GrowableSparseArrays, only a reduced
number of manipulations has been implemented.
"""
# Basic merge
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.ones(5))
gs_array2 = GrowableSparseArray(n_rows=1)
gs_array2.merge(gs_array)
assert_array_equal(gs_array.get_data()['row'],
gs_array2.get_data()['row'])
assert_array_equal(gs_array.get_data()['col'],
gs_array2.get_data()['col'])
assert_array_equal(gs_array.get_data()['data'],
gs_array2.get_data()['data'])
# Merge list
gs_array = GrowableSparseArray(n_rows=2)
gs_array.append(0, np.ones(5))
gs_array2 = GrowableSparseArray(n_rows=2)
gs_array2.append(1, 2 * np.ones(5))
gs_array3 = GrowableSparseArray(n_rows=2)
gs_array3.merge([gs_array, gs_array2])
assert_array_equal(gs_array3.get_data()['row'],
np.array([0.] * 5 + [1.] * 5))
assert_array_equal(gs_array3.get_data()['col'], np.tile(np.arange(5), 2))
assert_array_equal(gs_array3.get_data()['data'],
np.array([1.] * 5 + [2.] * 5))
# failure case
assert_raises(TypeError, gs_array3.merge, [gs_array, gs_array2, "foo"])
# Test failure case (merging arrays with different n_rows)
gs_array_wrong = GrowableSparseArray(n_rows=2)
gs_array_wrong.append(0, np.ones(5))
gs_array_wrong.append(1, np.ones(5))
gs_array = GrowableSparseArray(n_rows=1)
assert_raises(ValueError, gs_array.merge, gs_array_wrong)
# Test failure case (merge a numpy array)
gs_array = GrowableSparseArray(n_rows=1)
assert_raises(TypeError, gs_array.merge, np.ones(5))
def test_gsarray_merge():
"""This function tests GrowableSparseArray merging.
Because of the specific usage of GrowableSparseArrays, only a reduced
number of manipulations has been implemented.
"""
# Basic merge
gs_array = GrowableSparseArray(n_rows=1)
gs_array.append(0, np.ones(5))
gs_array2 = GrowableSparseArray(n_rows=1)
gs_array2.merge(gs_array)
assert_array_equal(gs_array.get_data()['row'], gs_array2.get_data()['row'])
assert_array_equal(gs_array.get_data()['col'], gs_array2.get_data()['col'])
assert_array_equal(gs_array.get_data()['data'],
gs_array2.get_data()['data'])
# Merge list
gs_array = GrowableSparseArray(n_rows=2)
gs_array.append(0, np.ones(5))
gs_array2 = GrowableSparseArray(n_rows=2)
gs_array2.append(1, 2 * np.ones(5))
gs_array3 = GrowableSparseArray(n_rows=2)
gs_array3.merge([gs_array, gs_array2])
assert_array_equal(gs_array3.get_data()['row'],
np.array([0.] * 5 + [1.] * 5))
assert_array_equal(gs_array3.get_data()['col'], np.tile(np.arange(5), 2))
assert_array_equal(gs_array3.get_data()['data'],
np.array([1.] * 5 + [2.] * 5))
# failure case
assert_raises(TypeError, gs_array3.merge, [gs_array, gs_array2, "foo"])
# Test failure case (merging arrays with different n_rows)
gs_array_wrong = GrowableSparseArray(n_rows=2)
gs_array_wrong.append(0, np.ones(5))
gs_array_wrong.append(1, np.ones(5))
gs_array = GrowableSparseArray(n_rows=1)
assert_raises(ValueError, gs_array.merge, gs_array_wrong)
# Test failure case (merge a numpy array)
gs_array = GrowableSparseArray(n_rows=1)
assert_raises(TypeError, gs_array.merge, np.ones(5))
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_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])