def test_KDAKernel_smoke(testdata_cbma):
"""Smoke test for nimare.meta.kernel.KDAKernel."""
kern = kernel.KDAKernel()
ma_maps = kern.transform(testdata_cbma.coordinates, testdata_cbma.masker, return_type="image")
assert len(ma_maps) == len(testdata_cbma.ids) - 2
ma_maps = kern.transform(testdata_cbma.coordinates, testdata_cbma.masker, return_type="array")
assert ma_maps.shape[0] == len(testdata_cbma.ids) - 2
def test_kdakernel_inputdataset_returndataset(testdata_cbma, tmp_path_factory):
"""
Check that the different return types produce equivalent results
(minus the masking element).
"""
tmpdir = tmp_path_factory.mktemp("test_kdakernel_inputdataset_returndataset")
testdata_cbma.update_path(tmpdir)
kern = kernel.KDAKernel(r=4, value=1)
# MA map generation from transformer
ma_maps = kern.transform(testdata_cbma, return_type="image")
ma_arr = kern.transform(testdata_cbma, return_type="array")
dset = kern.transform(testdata_cbma, return_type="dataset")
# Load generated MA maps
ma_maps_from_dset = kern.transform(dset, return_type="image")
ma_arr_from_dset = kern.transform(dset, return_type="array")
dset_from_dset = kern.transform(dset, return_type="dataset")
ma_maps_arr = testdata_cbma.masker.transform(ma_maps)
ma_maps_from_dset_arr = dset.masker.transform(ma_maps_from_dset)
ma_maps_dset = testdata_cbma.masker.transform(
dset.get_images(ids=dset.ids, imtype=kern.image_type)
)
assert isinstance(dset_from_dset, Dataset)
assert np.array_equal(ma_arr, ma_maps_arr)
assert np.array_equal(ma_arr, ma_maps_dset)
assert np.array_equal(ma_arr, ma_maps_from_dset_arr)
assert np.array_equal(ma_arr, ma_arr_from_dset)
def test_KDAKernel_transform_attributes(testdata_cbma):
"""Check that attributes are added at transform."""
kern = kernel.KDAKernel(r=4, value=1)
assert not hasattr(kern, "filename_pattern")
assert not hasattr(kern, "image_type")
_ = kern.transform(testdata_cbma, return_type="image")
assert hasattr(kern, "filename_pattern")
assert hasattr(kern, "image_type")
def test_KDAKernel_inputdataset_returnimages(testdata_cbma):
"""Centers of mass of KDA kernel maps should match the foci fed in.
This assumes focus isn't masked out and spheres don't overlap.
Test on Dataset object.
"""
id_ = "pain_03.nidm-1"
kern = kernel.KDAKernel(r=4, value=1)
ma_maps = kern.transform(testdata_cbma, return_type="image")
ijk = testdata_cbma.coordinates.loc[testdata_cbma.coordinates["id"] == id_, ["i", "j", "k"]]
ijk = np.squeeze(ijk.values.astype(int))
kern_data = ma_maps[0].get_fdata()
com = np.array(center_of_mass(kern_data)).astype(int).T
com = np.squeeze(com)
assert np.array_equal(ijk, com)
def test_kdakernel_2mm(testdata_cbma):
"""
COMs of KDA kernel maps should match the foci fed in (assuming focus isn't
masked out and spheres don't overlap).
Test on 2mm template.
"""
id_ = "pain_01.nidm-1"
kern = kernel.KDAKernel(r=4, value=1)
ma_maps = kern.transform(testdata_cbma.coordinates, testdata_cbma.masker, return_type="image")
ijk = testdata_cbma.coordinates.loc[testdata_cbma.coordinates["id"] == id_, ["i", "j", "k"]]
ijk = np.squeeze(ijk.values.astype(int))
kern_data = ma_maps[0].get_fdata()
com = np.array(center_of_mass(kern_data)).astype(int).T
com = np.squeeze(com)
assert np.array_equal(ijk, com)
#
# CBMA kernels are available as {py:class}`~nimare.meta.kernel.KernelTransformer`s in the {py:mod}`nimare.meta.kernel` module.
# There are three standard kernels that are currently available: {py:class}`~nimare.meta.kernel.MKDAKernel`, {py:class}`~nimare.meta.kernel.KDAKernel`, and {py:class}`~nimare.meta.kernel.ALEKernel`.
# Each class may be configured with certain parameters when a new object is initialized.
# For example, `MKDAKernel` accepts an `r` parameter, which determines the radius of the spheres that will be created around each peak coordinate.
# `ALEKernel` automatically uses the sample size associated with each experiment in the `Dataset` to determine the appropriate full-width-at-half-maximum of its Gaussian distribution, as described in {cite:t}`EICKHOFF20122349`; however, users may provide a constant `sample_size` or `fwhm` parameter when sample size information is not available within the `Dataset` metadata.
#
# Here we show how these three kernels can be applied to the same `Dataset`.
# In[2]:
from nimare.meta import kernel
mkda_kernel = kernel.MKDAKernel(r=10)
mkda_ma_maps = mkda_kernel.transform(sleuth_dset1)
kda_kernel = kernel.KDAKernel(r=10)
kda_ma_maps = kda_kernel.transform(sleuth_dset1)
ale_kernel = kernel.ALEKernel(sample_size=20)
ale_ma_maps = ale_kernel.transform(sleuth_dset1)
# In[3]:
# Here we delete the recent variables for the sake of reducing memory usage
del mkda_kernel, kda_kernel, ale_kernel
# In[4]:
# Generate figure
study_idx = 10 # a study with overlapping kernels
max_value = np.max(kda_ma_maps[study_idx].get_fdata()) + 1