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Add support for IBMA estimators with t-statistic images in Reports #896

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Aug 15, 2024
Merged

Add support for IBMA estimators with t-statistic images in Reports #896

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12bcf1e
Add support for IBMA estimators with t-statistic images in Reports
JulioAPeraza Aug 10, 2024
05de9c2
create mapping for the variables
jdkent Aug 14, 2024
bdcb42c
fix indentation
jdkent Aug 14, 2024
db8e0fe
run black
jdkent Aug 14, 2024
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14 changes: 10 additions & 4 deletions nimare/reports/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@
"method": "Method",
"alpha": "Alpha",
"prior": "Prior",
"tau2": "Between-study variance",
"use_sample_size": "Use sample size for weights",
"normalize_contrast_weights": "Normalize by the number of contrasts",
"two_sided": "Two-sided test",
Expand Down Expand Up @@ -483,10 +484,15 @@
)
elif meta_type == "IBMA":
# Use "z_maps", for Fishers, and Stouffers; otherwise use "beta_maps".
key_maps = "z_maps" if "z_maps" in self.results.estimator.inputs_ else "beta_maps"
INPUT_TYPE_LABELS = {"z_maps": "Z", "t_maps": "T", "beta_maps": "Beta"}
for key_maps, x_label in INPUT_TYPE_LABELS.items():
if key_maps in self.results.estimator.inputs_:
break
else:
key_maps, x_label = "beta_maps", "Beta"

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maps_arr = self.results.estimator.inputs_[key_maps]
ids_ = self.results.estimator.inputs_["id"]
x_label = "Z" if key_maps == "z_maps" else "Beta"

if self.results.estimator.aggressive_mask:
_plot_relcov_map(
Expand Down Expand Up @@ -526,12 +532,12 @@
if self.results.estimator.aggressive_mask:
voxel_mask = self.results.estimator.inputs_["aggressive_mask"]
corr = np.corrcoef(
self.results.estimator.inputs_["z_maps"][:, voxel_mask],
self.results.estimator.inputs_[key_maps][:, voxel_mask],
rowvar=True,
Comment on lines 532 to 536
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suggestion: Add a comment explaining the different approaches for aggressive and non-aggressive masking

The reasoning behind using different methods for correlation calculation based on the masking approach is not immediately clear. A brief comment would improve code understanding.

Suggested change
if self.results.estimator.aggressive_mask:
voxel_mask = self.results.estimator.inputs_["aggressive_mask"]
corr = np.corrcoef(
self.results.estimator.inputs_["z_maps"][:, voxel_mask],
self.results.estimator.inputs_[key_maps][:, voxel_mask],
rowvar=True,
# Calculate correlation based on masking approach
if self.results.estimator.aggressive_mask:
voxel_mask = self.results.estimator.inputs_["aggressive_mask"]
corr = np.corrcoef(
self.results.estimator.inputs_[key_maps][:, voxel_mask],
rowvar=True,
)
else:
corr = np.corrcoef(self.results.estimator.inputs_[key_maps], rowvar=True)

)
else:
corr = np.zeros((n_studies, n_studies), dtype=float)
for bag in self.results.estimator.inputs_["data_bags"]["z_maps"]:
for bag in self.results.estimator.inputs_["data_bags"][key_maps]:
study_bag = bag["study_mask"]
corr[np.ix_(study_bag, study_bag)] = np.corrcoef(
bag["values"],
Expand Down
27 changes: 23 additions & 4 deletions nimare/tests/test_reports.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
from nimare.correct import FWECorrector
from nimare.diagnostics import FocusCounter, Jackknife
from nimare.meta.cbma import ALESubtraction
from nimare.meta.ibma import Stouffers
from nimare.meta.ibma import FixedEffectsHedges, Stouffers
from nimare.reports.base import run_reports
from nimare.workflows import CBMAWorkflow, IBMAWorkflow, PairwiseCBMAWorkflow

Expand Down Expand Up @@ -75,17 +75,36 @@ def test_reports_ibma_smoke(tmp_path_factory, testdata_ibma, aggressive_mask):
"""Smoke test for IBMA reports."""
tmpdir = tmp_path_factory.mktemp("test_reports_ibma_smoke")

# Generate a report with z maps as inputs
stouffers_dir = op.join(tmpdir, "stouffers")
workflow = IBMAWorkflow(
estimator=Stouffers(aggressive_mask=aggressive_mask),
corrector="fdr",
diagnostics="jackknife",
voxel_thresh=3.2,
output_dir=tmpdir,
output_dir=stouffers_dir,
)
results = workflow.fit(testdata_ibma)

run_reports(results, tmpdir)
run_reports(results, stouffers_dir)

filename = "report.html"
outpath = op.join(tmpdir, filename)
outpath = op.join(stouffers_dir, filename)
assert op.isfile(outpath)
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suggestion (testing): Consider adding edge case tests for invalid or missing t-statistic images

While the current tests cover the basic functionality, it would be beneficial to add tests for edge cases such as invalid t-statistic images or missing images. This will ensure the robustness of the new feature.

def test_invalid_tstat_images():
    with pytest.raises(ValueError):
        IBMAWorkflow(invalid_image_path)

def test_missing_tstat_images():
    with pytest.raises(FileNotFoundError):
        IBMAWorkflow(non_existent_image_path)


# Generate a report with t maps as inputs
hedges_dir = op.join(tmpdir, "hedges")
workflow = IBMAWorkflow(
estimator=FixedEffectsHedges(aggressive_mask=aggressive_mask),
corrector="fdr",
diagnostics="jackknife",
voxel_thresh=3.2,
output_dir=hedges_dir,
)
results = workflow.fit(testdata_ibma)

run_reports(results, hedges_dir)

filename = "report.html"
outpath = op.join(hedges_dir, filename)
assert op.isfile(outpath)
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