MNT: np.bool -> bool

Author: effigies

nipype/algorithms/confounds.py

@@ -613,7 +613,7 @@ def _run_interface(self, runtime):
 
         if len(mask_images) == 0:
             img = nb.Nifti1Image(
-                np.ones(imgseries.shape[:3], dtype=np.bool),
+                np.ones(imgseries.shape[:3], dtype=bool),
                 affine=imgseries.affine,
                 header=imgseries.header,
             )
@@ -835,7 +835,7 @@ def _process_masks(self, mask_images, timeseries=None):
         self._mask_files = []
         timeseries = np.asanyarray(timeseries)
         for i, img in enumerate(mask_images):
-            mask = np.asanyarray(img.dataobj).astype(np.bool)
+            mask = np.asanyarray(img.dataobj).astype(bool)
             imgseries = timeseries[mask, :]
             imgseries = regress_poly(2, imgseries)[0]
             tSTD = _compute_tSTD(imgseries, 0, axis=-1)
@@ -1379,7 +1379,7 @@ def compute_noise_components(
     md_retained = []
 
     for name, img in zip(mask_names, mask_images):
-        mask = np.asanyarray(nb.squeeze_image(img).dataobj).astype(np.bool)
+        mask = np.asanyarray(nb.squeeze_image(img).dataobj).astype(bool)
         if imgseries.shape[:3] != mask.shape:
             raise ValueError(
                 "Inputs for CompCor, timeseries and mask, do not have "

nipype/algorithms/metrics.py

@@ -88,10 +88,10 @@ def _get_coordinates(self, data, affine):
     def _eucl_min(self, nii1, nii2):
         from scipy.spatial.distance import cdist, euclidean
 
-        origdata1 = np.asanyarray(nii1.dataobj).astype(np.bool)
+        origdata1 = np.asanyarray(nii1.dataobj).astype(bool)
         border1 = self._find_border(origdata1)
 
-        origdata2 = np.asanyarray(nii2.dataobj).astype(np.bool)
+        origdata2 = np.asanyarray(nii2.dataobj).astype(bool)
         border2 = self._find_border(origdata2)
 
         set1_coordinates = self._get_coordinates(border1, nii1.affine)
@@ -134,10 +134,10 @@ def _eucl_cog(self, nii1, nii2):
     def _eucl_mean(self, nii1, nii2, weighted=False):
         from scipy.spatial.distance import cdist
 
-        origdata1 = np.asanyarray(nii1.dataobj).astype(np.bool)
+        origdata1 = np.asanyarray(nii1.dataobj).astype(bool)
         border1 = self._find_border(origdata1)
 
-        origdata2 = np.asanyarray(nii2.dataobj).astype(np.bool)
+        origdata2 = np.asanyarray(nii2.dataobj).astype(bool)
 
         set1_coordinates = self._get_coordinates(border1, nii1.affine)
         set2_coordinates = self._get_coordinates(origdata2, nii2.affine)