RF: NipyBaseInterface

Author: effigies

nipype/interfaces/nipy/base.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+""" Base interface for nipy """
+
+from ..base import LibraryBaseInterface
+from ...utils.misc import package_check
+
+# Originally set in model, preprocess and utils
+# Set here to be imported, in case anybody depends on its presence
+# Remove in 2.0
+have_nipy = True
+try:
+    package_check('nipy')
+except ImportError:
+    have_nipy = False
+
+
+class NipyBaseInterface(LibraryBaseInterface):
+    _pkg = 'nipy'

nipype/interfaces/nipy/model.py

@@ -8,27 +8,12 @@
 import nibabel as nb
 import numpy as np
 
-from ...utils.misc import package_check
 from ...utils import NUMPY_MMAP
 
-from ..base import (BaseInterface, TraitedSpec, traits, File, OutputMultiPath,
+from .base import NipyBaseInterface, have_nipy
+from ..base import (TraitedSpec, traits, File, OutputMultiPath,
                     BaseInterfaceInputSpec, isdefined)
 
-have_nipy = True
-try:
-    package_check('nipy')
-except Exception as e:
-    have_nipy = False
-else:
-    import nipy.modalities.fmri.design_matrix as dm
-    import nipy.modalities.fmri.glm as GLM
-
-if have_nipy:
-    try:
-        BlockParadigm = dm.BlockParadigm
-    except AttributeError:
-        from nipy.modalities.fmri.experimental_paradigm import BlockParadigm
-
 
 class FitGLMInputSpec(BaseInterfaceInputSpec):
     session_info = traits.List(
@@ -94,14 +79,20 @@ class FitGLMOutputSpec(TraitedSpec):
     a = File(exists=True)
 
 
-class FitGLM(BaseInterface):
+class FitGLM(NipyBaseInterface):
     '''
     Fit GLM model based on the specified design. Supports only single or concatenated runs.
     '''
     input_spec = FitGLMInputSpec
     output_spec = FitGLMOutputSpec
 
     def _run_interface(self, runtime):
+        import nipy.modalities.fmri.glm as GLM
+        import nipy.modalities.fmri.design_matrix as dm
+        try:
+            BlockParadigm = dm.BlockParadigm
+        except AttributeError:
+            from nipy.modalities.fmri.experimental_paradigm import BlockParadigm
 
         session_info = self.inputs.session_info
 
@@ -283,14 +274,15 @@ class EstimateContrastOutputSpec(TraitedSpec):
     p_maps = OutputMultiPath(File(exists=True))
 
 
-class EstimateContrast(BaseInterface):
+class EstimateContrast(NipyBaseInterface):
     '''
     Estimate contrast of a fitted model.
     '''
     input_spec = EstimateContrastInputSpec
     output_spec = EstimateContrastOutputSpec
 
     def _run_interface(self, runtime):
+        import nipy.modalities.fmri.glm as GLM
 
         beta_nii = nb.load(self.inputs.beta)
         if isdefined(self.inputs.mask):

nipype/interfaces/nipy/preprocess.py

@@ -8,22 +8,12 @@
 import nibabel as nb
 import numpy as np
 
-from ...utils.misc import package_check
 from ...utils import NUMPY_MMAP
-
 from ...utils.filemanip import split_filename, fname_presuffix
-from ..base import (TraitedSpec, BaseInterface, traits, BaseInterfaceInputSpec,
-                    isdefined, File, InputMultiPath, OutputMultiPath)
 
-have_nipy = True
-try:
-    package_check('nipy')
-except Exception as e:
-    have_nipy = False
-else:
-    import nipy
-    from nipy import save_image, load_image
-    nipy_version = nipy.__version__
+from .base import NipyBaseInterface, have_nipy
+from ..base import (TraitedSpec, traits, BaseInterfaceInputSpec,
+                    isdefined, File, InputMultiPath, OutputMultiPath)
 
 
 class ComputeMaskInputSpec(BaseInterfaceInputSpec):
@@ -44,7 +34,7 @@ class ComputeMaskOutputSpec(TraitedSpec):
     brain_mask = File(exists=True)
 
 
-class ComputeMask(BaseInterface):
+class ComputeMask(NipyBaseInterface):
     input_spec = ComputeMaskInputSpec
     output_spec = ComputeMaskOutputSpec
 
@@ -118,7 +108,7 @@ class SpaceTimeRealignerOutputSpec(TraitedSpec):
               "euler angles"))
 
 
-class SpaceTimeRealigner(BaseInterface):
+class SpaceTimeRealigner(NipyBaseInterface):
     """Simultaneous motion and slice timing correction algorithm
 
     If slice_times is not specified, this algorithm performs spatial motion
@@ -157,11 +147,8 @@ class SpaceTimeRealigner(BaseInterface):
     output_spec = SpaceTimeRealignerOutputSpec
     keywords = ['slice timing', 'motion correction']
 
-    @property
-    def version(self):
-        return nipy_version
-
     def _run_interface(self, runtime):
+        from nipy import save_image, load_image
         all_ims = [load_image(fname) for fname in self.inputs.in_file]
 
         if not isdefined(self.inputs.slice_times):
@@ -233,7 +220,7 @@ class TrimOutputSpec(TraitedSpec):
     out_file = File(exists=True)
 
 
-class Trim(BaseInterface):
+class Trim(NipyBaseInterface):
     """ Simple interface to trim a few volumes from a 4d fmri nifti file
 
     Examples

nipype/interfaces/nipy/utils.py

@@ -5,19 +5,10 @@
 import warnings
 import nibabel as nb
 
-from ...utils.misc import package_check
-from ..base import (TraitedSpec, BaseInterface, traits, BaseInterfaceInputSpec,
+from .base import NipyBaseInterface, have_nipy
+from ..base import (TraitedSpec, traits, BaseInterfaceInputSpec,
                     File, isdefined)
 
-have_nipy = True
-try:
-    package_check('nipy')
-except Exception as e:
-    have_nipy = False
-else:
-    from nipy.algorithms.registration.histogram_registration import HistogramRegistration
-    from nipy.algorithms.registration.affine import Affine
-
 
 class SimilarityInputSpec(BaseInterfaceInputSpec):
     volume1 = File(exists=True, desc="3D volume", mandatory=True)
@@ -42,7 +33,7 @@ class SimilarityOutputSpec(TraitedSpec):
     similarity = traits.Float(desc="Similarity between volume 1 and 2")
 
 
-class Similarity(BaseInterface):
+class Similarity(NipyBaseInterface):
     """Calculates similarity between two 3D volumes. Both volumes have to be in
     the same coordinate system, same space within that coordinate system and
     with the same voxel dimensions.
@@ -72,6 +63,8 @@ def __init__(self, **inputs):
         super(Similarity, self).__init__(**inputs)
 
     def _run_interface(self, runtime):
+        from nipy.algorithms.registration.histogram_registration import HistogramRegistration
+        from nipy.algorithms.registration.affine import Affine
 
         vol1_nii = nb.load(self.inputs.volume1)
         vol2_nii = nb.load(self.inputs.volume2)