fix: allow multiple masks for interface

Author: mgxd

nipype/algorithms/confounds.py

@@ -302,7 +302,7 @@ def _list_outputs(self):
 class CompCorInputSpec(BaseInterfaceInputSpec):
     realigned_file = File(exists=True, mandatory=True,
                           desc='already realigned brain image (4D)')
-    mask_file = File(exists=True, desc='mask file that determines ROI (3D)')
+    mask_file = InputMultiPath(File(exists=True, desc='mask file(s) that determines ROI (3D)'))
     components_file = File('components_file.txt', exists=False,
                            usedefault=True,
                            desc='filename to store physiological components')
@@ -351,36 +351,43 @@ class CompCor(BaseInterface):
 
     def _run_interface(self, runtime):
         imgseries = nb.load(self.inputs.realigned_file, mmap=NUMPY_MMAP).get_data()
-        mask = nb.load(self.inputs.mask_file, mmap=NUMPY_MMAP).get_data()
-
-        if imgseries.shape[:3] != mask.shape:
-            raise ValueError('Inputs for CompCor, func {} and mask {}, do not have matching '
-                             'spatial dimensions ({} and {}, respectively)'
-                             .format(self.inputs.realigned_file, self.inputs.mask_file,
-                                     imgseries.shape[:3], mask.shape))
-
-        voxel_timecourses = imgseries[mask > 0]
-        # Zero-out any bad values
-        voxel_timecourses[np.isnan(np.sum(voxel_timecourses, axis=1)), :] = 0
-
-        # from paper:
-        # "The constant and linear trends of the columns in the matrix M were
-        # removed [prior to ...]"
-        degree = self.inputs.regress_poly_degree if self.inputs.use_regress_poly else 0
-        voxel_timecourses = regress_poly(degree, voxel_timecourses)
-
-        # "Voxel time series from the noise ROI (either anatomical or tSTD) were
-        # placed in a matrix M of size Nxm, with time along the row dimension
-        # and voxels along the column dimension."
-        M = voxel_timecourses.T
-
-        # "[... were removed] prior to column-wise variance normalization."
-        M = M / self._compute_tSTD(M, 1.)
-
-        # "The covariance matrix C = MMT was constructed and decomposed into its
-        # principal components using a singular value decomposition."
-        u, _, _ = linalg.svd(M, full_matrices=False)
-        components = u[:, :self.inputs.num_components]
+        components = None
+        
+        for mask_file in self.inputs.mask_file:
+            mask = nb.load(mask_file, mmap=NUMPY_MMAP).get_data()
+
+            if imgseries.shape[:3] != mask.shape:
+                raise ValueError('Inputs for CompCor, func {} and mask {}, do not have matching '
+                                 'spatial dimensions ({} and {}, respectively)'
+                                 .format(self.inputs.realigned_file, mask_file,
+                                         imgseries.shape[:3], mask.shape))
+
+            voxel_timecourses = imgseries[mask > 0]
+            # Zero-out any bad values
+            voxel_timecourses[np.isnan(np.sum(voxel_timecourses, axis=1)), :] = 0
+
+            # from paper:
+            # "The constant and linear trends of the columns in the matrix M were
+            # removed [prior to ...]"
+            degree = self.inputs.regress_poly_degree if self.inputs.use_regress_poly else 0
+            voxel_timecourses = regress_poly(degree, voxel_timecourses)
+
+            # "Voxel time series from the noise ROI (either anatomical or tSTD) were
+            # placed in a matrix M of size Nxm, with time along the row dimension
+            # and voxels along the column dimension."
+            M = voxel_timecourses.T
+
+            # "[... were removed] prior to column-wise variance normalization."
+            M = M / self._compute_tSTD(M, 1.)
+
+            # "The covariance matrix C = MMT was constructed and decomposed into its
+            # principal components using a singular value decomposition."
+            u, _, _ = linalg.svd(M, full_matrices=False)
+            if components is None:
+                components = u[:, :self.inputs.num_components]
+            else:
+                components = np.hstack((components, u[:, :self.inputs.num_components]))
+
         components_file = os.path.join(os.getcwd(), self.inputs.components_file)
 
         self._set_header()

nipype/pipeline/plugins/slurm.py

@@ -15,6 +15,7 @@
 from ...interfaces.base import CommandLine
 from .base import (SGELikeBatchManagerBase, logger, iflogger, logging)
 
+import subprocess
 
 
 class SLURMPlugin(SGELikeBatchManagerBase):
@@ -60,10 +61,11 @@ def __init__(self, **kwargs):
 
     def _is_pending(self, taskid):
         #  subprocess.Popen requires taskid to be a string
-        res = CommandLine('squeue',
-                          args=' '.join(['-j', '%s' % taskid]),
-                          terminal_output='allatonce').run()
-        return res.runtime.stdout.find(str(taskid)) > -1
+        proc = subprocess.Popen(["squeue", '-j', '%s' % taskid],
+                                stdout=subprocess.PIPE,
+                                stderr=subprocess.PIPE)
+        o, _ = proc.communicate()
+        return o.find(str(taskid)) > -1
 
     def _submit_batchtask(self, scriptfile, node):
         """
@@ -127,3 +129,4 @@ def _submit_batchtask(self, scriptfile, node):
         self._pending[taskid] = node.output_dir()
         logger.debug('submitted sbatch task: %d for node %s' % (taskid, node._id))
         return taskid
+