Refactor Fishers

nimare/meta/ibma.py

@@ -222,6 +222,21 @@ def _generate_description(self):
         )
         return description
 
+    def _fit_model(self, stat_maps):
+        """Fit the model to the data."""
+        n_studies, n_voxels = stat_maps.shape
+
+        pymare_dset = pymare.Dataset(y=stat_maps)
+        est = pymare.estimators.FisherCombinationTest()
+        est.fit_dataset(pymare_dset)
+        est_summary = est.summary()
+
+        z_map = est_summary.z.squeeze()
+        p_map = est_summary.p.squeeze()
+        dof_map = np.tile(n_studies - 1, n_voxels).astype(np.int32)
+
+        return z_map, p_map, dof_map
+
     def _fit(self, dataset):
         self.dataset = dataset
         self.masker = self.masker or dataset.masker
@@ -234,32 +249,25 @@ def _fit(self, dataset):
             )
 
         if self.aggressive_mask:
-            pymare_dset = pymare.Dataset(y=self.inputs_["z_maps"])
-            est = pymare.estimators.FisherCombinationTest()
-            est.fit_dataset(pymare_dset)
-            est_summary = est.summary()
+            voxel_mask = self.inputs_["aggressive_mask"]
 
-            z_map = _boolean_unmask(est_summary.z.squeeze(), self.inputs_["aggressive_mask"])
-            p_map = _boolean_unmask(est_summary.p.squeeze(), self.inputs_["aggressive_mask"])
-            dof_map = np.tile(
-                self.inputs_["z_maps"].shape[0] - 1,
-                self.inputs_["z_maps"].shape[1],
-            ).astype(np.int32)
-            dof_map = _boolean_unmask(dof_map, self.inputs_["aggressive_mask"])
+            z_map, p_map, dof_map = self._fit_model(self.inputs_["z_maps"][:, voxel_mask])
+
+            z_map = _boolean_unmask(z_map, voxel_mask)
+            p_map = _boolean_unmask(p_map, voxel_mask)
+            dof_map = _boolean_unmask(dof_map, voxel_mask)
 
         else:
             n_total_voxels = self.inputs_["z_maps"].shape[1]
             z_map = np.zeros(n_total_voxels, dtype=float)
             p_map = np.zeros(n_total_voxels, dtype=float)
             dof_map = np.zeros(n_total_voxels, dtype=np.int32)
             for bag in self.inputs_["data_bags"]["z_maps"]:
-                pymare_dset = pymare.Dataset(y=bag["values"])
-                est = pymare.estimators.FisherCombinationTest()
-                est.fit_dataset(pymare_dset)
-                est_summary = est.summary()
-                z_map[bag["voxel_mask"]] = est_summary.z.squeeze()
-                p_map[bag["voxel_mask"]] = est_summary.p.squeeze()
-                dof_map[bag["voxel_mask"]] = bag["values"].shape[0] - 1
+                z_map_temp, p_map_temp, dof_map_temp = self._fit_model(bag["values"])
+
+                z_map[bag["voxel_mask"]] = z_map_temp
+                p_map[bag["voxel_mask"]] = p_map_temp
+                dof_map[bag["voxel_mask"]] = dof_map_temp
 
         maps = {"z": z_map, "p": p_map, "dof": dof_map}
         description = self._generate_description()
@@ -277,6 +285,7 @@ class Stouffers(IBMAEstimator):
     .. versionchanged:: 0.2.3
 
         * Add correction for multiple contrasts within a study.
+        * New parameter: ``use_group_size`` to use publication group sizes for weights.
 
     .. versionchanged:: 0.2.1
 
@@ -339,6 +348,8 @@ def __init__(self, use_sample_size=False, use_group_size=False, **kwargs):
         if self.use_sample_size:
             self._required_inputs["sample_sizes"] = ("metadata", "sample_sizes")
 
+        self.use_group_size = use_group_size
+
     def _preprocess_input(self, dataset):
         """Preprocess additional inputs to the Estimator from the Dataset as needed."""
         super()._preprocess_input(dataset)
@@ -419,12 +430,11 @@ def _fit(self, dataset):
             corr = np.corrcoef(self.inputs_["z_maps"], rowvar=True)
 
         if self.aggressive_mask:
-            stat_maps = self.inputs_["z_maps"]
             study_mask = self.dataset.images["id"].isin(self.inputs_["id"])
             voxel_mask = self.inputs_["aggressive_mask"]
 
             z_map, p_map, dof_map = self._fit_model(
-                stat_maps[:, voxel_mask],
+                self.inputs_["z_maps"][:, voxel_mask],
                 study_mask,
                 corr=corr,
             )
@@ -439,12 +449,9 @@ def _fit(self, dataset):
             p_map = np.zeros(n_voxels, dtype=float)
             dof_map = np.zeros(n_voxels, dtype=np.int32)
             for bag in self.inputs_["data_bags"]["z_maps"]:
-                study_mask = bag["study_mask"]
-                voxel_mask = bag["voxel_mask"]
-
                 z_map_temp, p_map_temp, dof_map_temp = self._fit_model(
                     bag["values"],
-                    study_mask,
+                    bag["study_mask"],
                     corr=corr,
                 )