def test_ALE_ma_map_reuse(testdata_cbma, tmp_path_factory, caplog):
"""Test that MA maps are re-used when appropriate."""
from nimare.meta import kernel
tmpdir = tmp_path_factory.mktemp("test_ALE_ma_map_reuse")
testdata_cbma.update_path(tmpdir)
# ALEKernel cannot extract sample_size from a Dataset,
# so we need to set it for this kernel and for the later meta-analyses.
kern = kernel.ALEKernel(sample_size=20)
dset = kern.transform(testdata_cbma, return_type="dataset")
# The associated column should be in the new Dataset's images DataFrame
cols = dset.images.columns.tolist()
assert any(["ALEKernel" in col for col in cols])
# The Dataset without the images will generate them from scratch.
# If drop_invalid is False, then there should be an Exception, since two studies in the test
# dataset are missing coordinates.
meta = ale.ALE(kernel__sample_size=20)
with pytest.raises(Exception):
meta.fit(testdata_cbma, drop_invalid=False)
with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"):
meta.fit(testdata_cbma)
assert "Loading pre-generated MA maps" not in caplog.text
# The Dataset with the images will re-use them, as evidenced by the logger message.
with caplog.at_level(logging.DEBUG, logger="nimare.meta.cbma.base"):
meta.fit(dset)
assert "Loading pre-generated MA maps" in caplog.text
def test_ALE_approximate_null_unit(testdata_cbma, tmp_path_factory):
"""Unit test for ALE with approximate null_method."""
tmpdir = tmp_path_factory.mktemp("test_ALE_approximate_null_unit")
out_file = os.path.join(tmpdir, "file.pkl.gz")
meta = ale.ALE(null_method="approximate")
res = meta.fit(testdata_cbma)
assert "stat" in res.maps.keys()
assert "p" in res.maps.keys()
assert "z" in res.maps.keys()
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(res.get_map("z", return_type="image"), nib.Nifti1Image)
assert isinstance(res.get_map("z", return_type="array"), np.ndarray)
res2 = res.copy()
assert res2 != res
assert isinstance(res, nimare.results.MetaResult)
# Test saving/loading
meta.save(out_file, compress=True)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=True)
assert isinstance(meta2, ale.ALE)
with pytest.raises(pickle.UnpicklingError):
ale.ALE.load(out_file, compressed=False)
meta.save(out_file, compress=False)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=False)
assert isinstance(meta2, ale.ALE)
with pytest.raises(OSError):
ale.ALE.load(out_file, compressed=True)
# Test MCC methods
# Monte Carlo FWE
corr = FWECorrector(method="montecarlo", voxel_thresh=0.001, n_iters=5, n_cores=-1)
cres = corr.transform(meta.results)
assert isinstance(cres, nimare.results.MetaResult)
assert "z_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "z_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert isinstance(
cres.get_map("z_level-cluster_corr-FWE_method-montecarlo", return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_level-cluster_corr-FWE_method-montecarlo", return_type="array"), np.ndarray
)
# Bonferroni FWE
corr = FWECorrector(method="bonferroni")
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="image"), nib.Nifti1Image
)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="array"), np.ndarray
)
# FDR
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(meta.results)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="image"), nib.Nifti1Image
)
assert isinstance(cres.get_map("z_corr-FDR_method-indep", return_type="array"), np.ndarray)
def test_ALE_montecarlo_null_unit(testdata_cbma, tmp_path_factory):
"""Unit test for ALE with an montecarlo null_method.
This test is run with low-memory kernel transformation as well.
"""
tmpdir = tmp_path_factory.mktemp("test_ALE_montecarlo_null_unit")
out_file = os.path.join(tmpdir, "file.pkl.gz")
meta = ale.ALE(null_method="montecarlo",
n_iters=10,
kernel__memory_limit="1gb")
res = meta.fit(testdata_cbma)
assert "stat" in res.maps.keys()
assert "p" in res.maps.keys()
assert "z" in res.maps.keys()
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(res.get_map("z", return_type="image"), nib.Nifti1Image)
assert isinstance(res.get_map("z", return_type="array"), np.ndarray)
res2 = res.copy()
assert res2 != res
assert isinstance(res, nimare.results.MetaResult)
# Test saving/loading
meta.save(out_file, compress=True)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=True)
assert isinstance(meta2, ale.ALE)
with pytest.raises(pickle.UnpicklingError):
ale.ALE.load(out_file, compressed=False)
meta.save(out_file, compress=False)
assert os.path.isfile(out_file)
meta2 = ale.ALE.load(out_file, compressed=False)
assert isinstance(meta2, ale.ALE)
with pytest.raises(OSError):
ale.ALE.load(out_file, compressed=True)
# Test MCC methods
# Monte Carlo FWE
corr = FWECorrector(method="montecarlo",
voxel_thresh=0.001,
n_iters=5,
n_cores=-1)
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert "z_desc-size_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "z_desc-mass_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "z_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert "logp_desc-size_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "logp_desc-mass_level-cluster_corr-FWE_method-montecarlo" in cres.maps.keys(
)
assert "logp_level-voxel_corr-FWE_method-montecarlo" in cres.maps.keys()
assert isinstance(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo",
return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo",
return_type="array"),
np.ndarray,
)
assert isinstance(
cres.get_map("z_desc-mass_level-cluster_corr-FWE_method-montecarlo",
return_type="image"),
nib.Nifti1Image,
)
assert isinstance(
cres.get_map("z_desc-mass_level-cluster_corr-FWE_method-montecarlo",
return_type="array"),
np.ndarray,
)
# Check that the updated null distribution is in the corrected MetaResult's Estimator.
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
in cres.estimator.null_distributions_.keys())
# The updated null distribution should *not* be in the original Estimator, nor in the
# uncorrected MetaResult's Estimator.
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
not in meta.null_distributions_.keys())
assert ("values_desc-mass_level-cluster_corr-fwe_method-montecarlo"
not in res.estimator.null_distributions_.keys())
# Bonferroni FWE
corr = FWECorrector(method="bonferroni")
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="image"),
nib.Nifti1Image)
assert isinstance(
cres.get_map("z_corr-FWE_method-bonferroni", return_type="array"),
np.ndarray)
# FDR
corr = FDRCorrector(method="indep", alpha=0.05)
cres = corr.transform(res)
assert isinstance(cres, nimare.results.MetaResult)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="image"),
nib.Nifti1Image)
assert isinstance(
cres.get_map("z_corr-FDR_method-indep", return_type="array"),
np.ndarray)