def test_CorrelationDistributionDecoder_smoke(testdata_laird,
tmp_path_factory):
"""Smoke test for continuous.CorrelationDistributionDecoder."""
tmpdir = tmp_path_factory.mktemp("test_CorrelationDistributionDecoder")
testdata_laird = testdata_laird.copy()
features = testdata_laird.get_labels(ids=testdata_laird.ids[0])[:5]
decoder = continuous.CorrelationDistributionDecoder(features=features)
# No images of the requested type
with pytest.raises(ValueError):
decoder.fit(testdata_laird)
# Let's add the path
testdata_laird.update_path(tmpdir)
# Then let's make some images to decode
kern = kernel.MKDAKernel(r=10, value=1)
dset = kern.transform(testdata_laird, return_type="dataset")
# And now we have images we can use for decoding!
decoder = continuous.CorrelationDistributionDecoder(
features=features,
target_image=kern.image_type,
)
decoder.fit(dset)
# Make an image to decode
meta = mkda.KDA(null_method="approximate")
res = meta.fit(testdata_laird)
img = res.get_map("stat")
decoded_df = decoder.transform(img)
assert isinstance(decoded_df, pd.DataFrame)
def test_kda_density_fwe_1core(testdata_cbma):
"""
Smoke test for KDA
"""
meta = mkda.KDA()
res = meta.fit(testdata_cbma)
corr = FWECorrector(method="montecarlo", n_iters=5, n_cores=1)
cres = corr.transform(res)
assert isinstance(res, nimare.results.MetaResult)
assert isinstance(cres, nimare.results.MetaResult)
def test_CorrelationDecoder_smoke(testdata_laird):
"""Smoke test for continuous.CorrelationDecoder."""
testdata_laird = testdata_laird.copy()
features = testdata_laird.get_labels(ids=testdata_laird.ids[0])[:5]
decoder = continuous.CorrelationDecoder(features=features)
decoder.fit(testdata_laird)
# Make an image to decode
meta = mkda.KDA(null_method="approximate")
res = meta.fit(testdata_laird)
img = res.get_map("stat")
decoded_df = decoder.transform(img)
assert isinstance(decoded_df, pd.DataFrame)
def test_CorrelationDecoder_smoke(testdata_laird, tmp_path_factory):
"""Smoke test for continuous.CorrelationDecoder."""
tmpdir = tmp_path_factory.mktemp("test_CorrelationDecoder")
testdata_laird = testdata_laird.copy()
features = testdata_laird.get_labels(ids=testdata_laird.ids[0])[:5]
decoder = continuous.CorrelationDecoder(features=features)
# No basepath
with pytest.raises(ValueError):
decoder.fit(testdata_laird)
# Let's add the path
testdata_laird.update_path(tmpdir)
decoder.fit(testdata_laird)
# Make an image to decode
meta = mkda.KDA(null_method="approximate")
res = meta.fit(testdata_laird)
img = res.get_map("stat")
decoded_df = decoder.transform(img)
assert isinstance(decoded_df, pd.DataFrame)