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

) * Add support for IBMA estimators with t-statistic images in Reports * create mapping for the variables Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> * fix indentation * run black --------- Co-authored-by: James Kent <[email protected]> Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>
neurostuff · Aug 15, 2024 · c1cd2d7 · c1cd2d7
1 parent 8dd152f
commit c1cd2d7
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Showing 2 changed files with 33 additions and 8 deletions.
diff --git a/nimare/reports/base.py b/nimare/reports/base.py
@@ -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",
@@ -483,10 +484,15 @@ def __init__(
                 )
             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"
+
                 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(
@@ -526,12 +532,12 @@ def __init__(
                     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,
                         )
                     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"],

diff --git a/nimare/tests/test_reports.py b/nimare/tests/test_reports.py
@@ -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
 
@@ -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)
+
+    # 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)