Fix the correlation matrix computation in Stouffers and Reports

nimare/meta/ibma.py

@@ -408,13 +408,10 @@ def _preprocess_input(self, dataset):
                     rowvar=True,
                 )
             else:
-                self.inputs_["corr_matrix"] = np.zeros((n_studies, n_studies), dtype=float)
-                for bag in self.inputs_["data_bags"]["z_maps"]:
-                    study_bag = bag["study_mask"]
-                    self.inputs_["corr_matrix"][np.ix_(study_bag, study_bag)] = np.corrcoef(
-                        bag["values"],
-                        rowvar=True,
-                    )
+                self.inputs_["corr_matrix"] = np.corrcoef(
+                    self.inputs_["z_maps"],
+                    rowvar=True,
+                )
 
     def _generate_description(self):
         description = (

nimare/reports/base.py

@@ -528,21 +528,17 @@ def __init__(
 
                 # Compute similarity matrix
                 if self.results.estimator.inputs_["corr_matrix"] is None:
-                    n_studies = len(ids_)
                     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.zeros((n_studies, n_studies), dtype=float)
-                        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"],
-                                rowvar=True,
-                            )
+                        corr = np.corrcoef(
+                            self.results.estimator.inputs_[key_maps],
+                            rowvar=True,
+                        )
                 else:
                     corr = self.inputs_["corr_matrix"]
 

nimare/tests/test_meta_kernel.py

@@ -1,7 +1,5 @@
 """Test nimare.meta.kernel (CBMA kernel estimators)."""
 
-import time
-
 import nibabel as nib
 import numpy as np
 import pytest
@@ -177,24 +175,17 @@ def test_ALEKernel_memory(testdata_cbma, tmp_path_factory):
     ma_maps_cached = kern_cached.transform(coord, masker=testdata_cbma.masker, return_type="array")
 
     kern = kernel.ALEKernel(sample_size=20, memory=None)
-    start = time.time()
     ma_maps = kern.transform(coord, masker=testdata_cbma.masker, return_type="array")
-    done = time.time()
-    elapsed = done - start
 
     assert np.array_equal(ma_maps_cached, ma_maps)
 
     # Test that memory is actually used
     kern_cached_fast = kernel.ALEKernel(sample_size=20, memory=str(cachedir), memory_level=2)
-    start_chached = time.time()
     ma_maps_cached_fast = kern_cached_fast.transform(
         coord, masker=testdata_cbma.masker, return_type="array"
     )
-    done_cached = time.time()
-    elapsed_cached = done_cached - start_chached
 
     assert np.array_equal(ma_maps_cached_fast, ma_maps)
-    assert elapsed_cached < elapsed
 
 
 def test_MKDA_kernel_sum_across(testdata_cbma):