Update get_studies_by_mask.

examples/02_meta-analyses/07_macm.py

@@ -17,7 +17,7 @@
 from nilearn import datasets, image, plotting
 
 from nimare.correct import FWECorrector
-from nimare.dataset import Dataset
+from nimare.dataset import Dataset, DatasetSearcher
 from nimare.meta.cbma.ale import SCALE
 from nimare.meta.cbma.mkda import MKDAChi2
 
@@ -44,7 +44,8 @@
 ###############################################################################
 # Select studies with a reported coordinate in the ROI
 # -----------------------------------------------------------------------------
-roi_ids = dset.get_studies_by_mask(roi_img)
+searcher = DatasetSearcher()
+roi_ids = searcher.get_studies_by_mask(dset, roi_img)
 dset_sel = dset.slice(roi_ids)
 print(f"{len(roi_ids)}/{len(dset.ids)} studies report at least one coordinate in the ROI")
 

examples/04_decoding/01_plot_discrete_decoders.py

@@ -17,7 +17,7 @@
 import numpy as np
 from nilearn.plotting import plot_roi
 
-from nimare.dataset import Dataset
+from nimare.dataset import Dataset, DatasetSearcher
 from nimare.decode import discrete
 from nimare.utils import get_resource_path
 
@@ -40,7 +40,8 @@
 plot_roi(mask_img, draw_cross=False)
 
 # Get studies with voxels in the mask
-ids = dset.get_studies_by_mask(mask_img)
+searcher = DatasetSearcher()
+ids = searcher.get_studies_by_mask(dset, mask_img)
 
 ###############################################################################
 #

nimare/decode/continuous.py

@@ -8,14 +8,15 @@
 from nilearn.masking import apply_mask
 from tqdm.auto import tqdm
 
-from .. import references
-from ..base import Decoder
-from ..due import due
-from ..meta.cbma.base import CBMAEstimator
-from ..meta.cbma.mkda import MKDAChi2
-from ..stats import pearson
-from ..utils import _check_type, _safe_transform
-from .utils import weight_priors
+from nimare import references
+from nimare.base import Decoder
+from nimare.dataset import DatasetSearcher
+from nimare.decode.utils import weight_priors
+from nimare.due import due
+from nimare.meta.cbma.base import CBMAEstimator
+from nimare.meta.cbma.mkda import MKDAChi2
+from nimare.stats import pearson
+from nimare.utils import _check_type, _safe_transform
 
 LGR = logging.getLogger(__name__)
 
@@ -182,10 +183,12 @@ def _fit(self, dataset):
             Masked meta-analytic maps
         """
         self.masker = dataset.masker
+        searcher = DatasetSearcher()
 
         n_features = len(self.features_)
         for i_feature, feature in enumerate(tqdm(self.features_, total=n_features)):
-            feature_ids = dataset.get_studies_by_label(
+            feature_ids = searcher.get_studies_by_label(
+                dataset,
                 labels=[feature],
                 label_threshold=self.frequency_threshold,
             )
@@ -292,11 +295,12 @@ def _fit(self, dataset):
             Masked meta-analytic maps
         """
         self.masker = dataset.masker
+        searcher = DatasetSearcher()
 
         images_ = {}
         for feature in self.features_:
-            feature_ids = dataset.get_studies_by_label(
-                labels=[feature], label_threshold=self.frequency_threshold
+            feature_ids = searcher.get_studies_by_label(
+                dataset, labels=[feature], label_threshold=self.frequency_threshold
             )
             selected_ids = sorted(list(set(feature_ids).intersection(self.inputs_["id"])))
             selected_id_idx = [

nimare/workflows/macm.py

@@ -5,7 +5,7 @@
 from shutil import copyfile
 
 from ..correct import FWECorrector
-from ..dataset import Dataset
+from ..dataset import Dataset, DatasetSearcher
 from ..meta import ALE
 
 LGR = logging.getLogger(__name__)
@@ -17,7 +17,8 @@ def macm_workflow(
     """Perform MACM with ALE algorithm."""
     LGR.info("Loading coordinates...")
     dset = Dataset(dataset_file)
-    sel_ids = dset.get_studies_by_mask(mask_file)
+    searcher = DatasetSearcher()
+    sel_ids = searcher.get_studies_by_mask(dset, mask_file)
     sel_dset = dset.slice(sel_ids)
 
     # override sample size