rebased dipy and adapted old interfaces

Author: oesteban

nipype/interfaces/dipy/base.py

@@ -1,8 +1,44 @@
 # -*- coding: utf-8 -*-
-import numpy as np
+""" Base interfaces for dipy """
 import os.path as op
+import numpy as np
 from nipype.interfaces.base import (traits, File, isdefined,
                                     BaseInterface, BaseInterfaceInputSpec)
+from ... import logging
+
+IFLOGGER = logging.getLogger('interface')
+
+HAVE_DIPY = True
+try:
+    import dipy
+except ImportError:
+    HAVE_DIPY = False
+
+
+def no_dipy():
+    """ Check if dipy is available """
+    global HAVE_DIPY
+    return not HAVE_DIPY
+
+
+def dipy_version():
+    """ Check dipy version """
+    if no_dipy():
+        return None
+
+    return dipy.__version__
+
+
+class DipyBaseInterface(BaseInterface):
+
+    """
+    A base interface for py:mod:`dipy` computations
+    """
+    def __init__(self, **inputs):
+        if no_dipy():
+            IFLOGGER.error('dipy was not found')
+            # raise ImportError('dipy was not found')
+        super(DipyBaseInterface, self).__init__(**inputs)
 
 
 class DipyBaseInterfaceInputSpec(BaseInterfaceInputSpec):
@@ -13,7 +49,7 @@ class DipyBaseInterfaceInputSpec(BaseInterfaceInputSpec):
     out_prefix = traits.Str(desc=('output prefix for file names'))
 
 
-class DipyBaseInterface(BaseInterface):
+class DipyDiffusionInterface(DipyBaseInterface):
 
     """
     A base interface for py:mod:`dipy` computations

nipype/interfaces/dipy/preprocess.py

@@ -8,25 +8,13 @@
    >>> os.chdir(datadir)
 """
 import os.path as op
-import warnings
-
 import nibabel as nb
 import numpy as np
 
-from ..base import (traits, TraitedSpec, BaseInterface, File, isdefined)
-from ...utils.filemanip import split_filename
-from ...utils.misc import package_check
+from ..base import (traits, TraitedSpec, File, isdefined)
+from .base import DipyBaseInterface
 from ... import logging
-iflogger = logging.getLogger('interface')
-
-have_dipy = True
-try:
-    package_check('dipy', version='0.6.0')
-except Exception as e:
-    have_dipy = False
-else:
-    from dipy.align.aniso2iso import resample
-    from dipy.core.gradients import GradientTable
+IFLOGGER = logging.getLogger('interface')
 
 
 class ResampleInputSpec(TraitedSpec):
@@ -46,7 +34,7 @@ class ResampleOutputSpec(TraitedSpec):
     out_file = File(exists=True)
 
 
-class Resample(BaseInterface):
+class Resample(DipyBaseInterface):
 
     """
     An interface to reslicing diffusion datasets.
@@ -75,7 +63,7 @@ def _run_interface(self, runtime):
         resample_proxy(self.inputs.in_file, order=order,
                        new_zooms=vox_size, out_file=out_file)
 
-        iflogger.info('Resliced image saved as {i}'.format(i=out_file))
+        IFLOGGER.info('Resliced image saved as {i}'.format(i=out_file))
         return runtime
 
     def _list_outputs(self):
@@ -111,7 +99,7 @@ class DenoiseOutputSpec(TraitedSpec):
     out_file = File(exists=True)
 
 
-class Denoise(BaseInterface):
+class Denoise(DipyBaseInterface):
 
     """
     An interface to denoising diffusion datasets [Coupe2008]_.
@@ -166,7 +154,7 @@ def _run_interface(self, runtime):
                              smask=signal_mask,
                              nmask=noise_mask,
                              out_file=out_file)
-        iflogger.info(('Denoised image saved as {i}, estimated '
+        IFLOGGER.info(('Denoised image saved as {i}, estimated '
                        'SNR={s}').format(i=out_file, s=str(s)))
         return runtime
 
@@ -187,6 +175,7 @@ def resample_proxy(in_file, order=3, new_zooms=None, out_file=None):
     """
     Performs regridding of an image to set isotropic voxel sizes using dipy.
     """
+    from dipy.align.aniso2iso import resample
 
     if out_file is None:
         fname, fext = op.splitext(op.basename(in_file))
@@ -227,7 +216,6 @@ def nlmeans_proxy(in_file, settings,
     """
     Uses non-local means to denoise 4D datasets
     """
-    package_check('dipy', version='0.8.0.dev')
     from dipy.denoise.nlmeans import nlmeans
     from scipy.ndimage.morphology import binary_erosion
     from scipy import ndimage

nipype/interfaces/dipy/reconstruction.py

@@ -1,23 +1,18 @@
 # -*- coding: utf-8 -*-
-import os
+"""
+Interfaces to the reconstruction algorithms in dipy
+
+"""
 import os.path as op
 
 import numpy as np
-from dipy.core.gradients import GradientTable
 import nibabel as nb
 
-from nipype.interfaces.base import (TraitedSpec, File, InputMultiPath,
-                                    OutputMultiPath, Undefined, traits,
-                                    isdefined, OutputMultiPath,
-                                    CommandLineInputSpec, CommandLine,
-                                    BaseInterface, BaseInterfaceInputSpec,
-                                    traits)
-from nipype.utils.filemanip import split_filename, fname_presuffix
-
-from .base import DipyBaseInterface, DipyBaseInterfaceInputSpec
+from nipype.interfaces.base import TraitedSpec, File, traits, isdefined
+from .base import DipyDiffusionInterface, DipyBaseInterfaceInputSpec
 
 from nipype import logging
-iflogger = logging.getLogger('interface')
+IFLOGGER = logging.getLogger('interface')
 
 
 class RESTOREInputSpec(DipyBaseInterfaceInputSpec):
@@ -27,20 +22,20 @@ class RESTOREInputSpec(DipyBaseInterfaceInputSpec):
 
 
 class RESTOREOutputSpec(TraitedSpec):
-    fa = File(desc=('output fractional anisotropy (FA) map computed from '
-                    'the fitted DTI'))
-    md = File(desc=('output mean diffusivity (MD) map computed from the '
-                    'fitted DTI'))
-    rd = File(desc=('output radial diffusivity (RD) map computed from '
-                    'the fitted DTI'))
+    fa = File(desc='output fractional anisotropy (FA) map computed from '
+                   'the fitted DTI')
+    md = File(desc='output mean diffusivity (MD) map computed from the '
+                   'fitted DTI')
+    rd = File(desc='output radial diffusivity (RD) map computed from '
+                   'the fitted DTI')
     mode = File(desc=('output mode (MO) map computed from the fitted DTI'))
     trace = File(desc=('output the tensor trace map computed from the '
                        'fitted DTI'))
     evals = File(desc=('output the eigenvalues of the fitted DTI'))
     evecs = File(desc=('output the eigenvectors of the fitted DTI'))
 
 
-class RESTORE(DipyBaseInterface):
+class RESTORE(DipyDiffusionInterface):
 
     """
     Uses RESTORE [Chang2005]_ to perform DTI fitting with outlier detection.
@@ -92,7 +87,7 @@ def _run_interface(self, runtime):
             noise_msk = noise_msk.astype(np.uint8)
             try_b0 = False
         elif np.all(data[msk == 0, 0] == 0):
-            iflogger.info('Input data are masked.')
+            IFLOGGER.info('Input data are masked.')
             noise_msk = msk.reshape(-1).astype(np.uint8)
         else:
             noise_msk = (1 - msk).reshape(-1).astype(np.uint8)
@@ -123,18 +118,18 @@ def _run_interface(self, runtime):
         sigma = mean_std * (1 + bias)
 
         if sigma == 0:
-            iflogger.warn(
+            IFLOGGER.warn(
                 ('Noise std is 0.0, looks like data was masked and noise'
                  ' cannot be estimated correctly. Using default tensor '
                  'model instead of RESTORE.'))
             dti = TensorModel(gtab)
         else:
-            iflogger.info(('Performing RESTORE with noise std=%.4f.') % sigma)
+            IFLOGGER.info(('Performing RESTORE with noise std=%.4f.') % sigma)
             dti = TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
 
         try:
             fit_restore = dti.fit(data, msk)
-        except TypeError as e:
+        except TypeError:
             dti = TensorModel(gtab)
             fit_restore = dti.fit(data, msk)
 
@@ -181,7 +176,7 @@ class EstimateResponseSHOutputSpec(TraitedSpec):
     out_mask = File(exists=True, desc=('output wm mask'))
 
 
-class EstimateResponseSH(DipyBaseInterface):
+class EstimateResponseSH(DipyDiffusionInterface):
 
     """
     Uses dipy to compute the single fiber response to be used in spherical
@@ -253,14 +248,14 @@ def _run_interface(self, runtime):
             ratio = abs(response[1] / response[0])
 
         if ratio > 0.25:
-            iflogger.warn(('Estimated response is not prolate enough. '
+            IFLOGGER.warn(('Estimated response is not prolate enough. '
                            'Ratio=%0.3f.') % ratio)
         elif ratio < 1.e-5 or np.any(np.isnan(response)):
             response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
-            iflogger.warn(
+            IFLOGGER.warn(
                 ('Estimated response is not valid, using a default one'))
         else:
-            iflogger.info(('Estimated response: %s') % str(response[:3]))
+            IFLOGGER.info(('Estimated response: %s') % str(response[:3]))
 
         np.savetxt(op.abspath(self.inputs.response), response)
 
@@ -293,7 +288,7 @@ class CSDOutputSpec(TraitedSpec):
     out_fods = File(desc=('fODFs output file name'))
 
 
-class CSD(DipyBaseInterface):
+class CSD(DipyDiffusionInterface):
 
     """
     Uses CSD [Tournier2007]_ to generate the fODF of DWIs. The interface uses
@@ -344,13 +339,13 @@ def _run_interface(self, runtime):
         ratio = response[0][1] / response[0][0]
 
         if abs(ratio - 0.2) > 0.1:
-            iflogger.warn(('Estimated response is not prolate enough. '
+            IFLOGGER.warn(('Estimated response is not prolate enough. '
                            'Ratio=%0.3f.') % ratio)
 
         csd_model = ConstrainedSphericalDeconvModel(
             gtab, response, sh_order=self.inputs.sh_order)
 
-        iflogger.info('Fitting CSD model')
+        IFLOGGER.info('Fitting CSD model')
         csd_fit = csd_model.fit(data, msk)
 
         f = gzip.open(self._gen_filename('csdmodel', ext='.pklz'), 'wb')

nipype/interfaces/dipy/simulate.py

@@ -7,29 +7,18 @@
 """
 
 from __future__ import division
+from multiprocessing import (Pool, cpu_count)
+import os.path as op
 from builtins import range
 
-import os.path as op
-from multiprocessing import (Pool, cpu_count)
 
 import nibabel as nb
 
-from ..base import (traits, TraitedSpec, BaseInterface, BaseInterfaceInputSpec,
+from ..base import (traits, TraitedSpec, BaseInterfaceInputSpec,
                     File, InputMultiPath, isdefined)
-from ...utils.misc import package_check
+from .base import DipyBaseInterface
 from ... import logging
-iflogger = logging.getLogger('interface')
-
-have_dipy = True
-try:
-    package_check('dipy', version='0.8.0')
-except Exception as e:
-    have_dipy = False
-else:
-    import numpy as np
-    from dipy.sims.voxel import (multi_tensor, add_noise,
-                                 all_tensor_evecs)
-    from dipy.core.gradients import gradient_table
+IFLOGGER = logging.getLogger('interface')
 
 
 class SimulateMultiTensorInputSpec(BaseInterfaceInputSpec):
@@ -77,7 +66,7 @@ class SimulateMultiTensorOutputSpec(TraitedSpec):
     out_bval = File(exists=True, desc='simulated b values')
 
 
-class SimulateMultiTensor(BaseInterface):
+class SimulateMultiTensor(DipyBaseInterface):
 
     """
     Interface to MultiTensor model simulator in dipy
@@ -101,6 +90,8 @@ class SimulateMultiTensor(BaseInterface):
     output_spec = SimulateMultiTensorOutputSpec
 
     def _run_interface(self, runtime):
+        from dipy.core.gradients import gradient_table
+
         # Gradient table
         if isdefined(self.inputs.in_bval) and isdefined(self.inputs.in_bvec):
             # Load the gradient strengths and directions
@@ -237,7 +228,7 @@ def _run_interface(self, runtime):
             pool = Pool(processes=n_proc)
 
         # Simulate sticks using dipy
-        iflogger.info(('Starting simulation of %d voxels, %d diffusion'
+        IFLOGGER.info(('Starting simulation of %d voxels, %d diffusion'
                        ' directions.') % (len(args), ndirs))
         result = np.array(pool.map(_compute_voxel, args))
         if np.shape(result)[1] != ndirs:
@@ -280,6 +271,7 @@ def _compute_voxel(args):
     .. [Pierpaoli1996] Pierpaoli et al., Diffusion tensor MR imaging
       of the human brain, Radiology 201:637-648. 1996.
     """
+    from dipy.sims.voxel import multi_tensor
 
     ffs = args['fractions']
     gtab = args['gradients']
@@ -297,7 +289,7 @@ def _compute_voxel(args):
                 angles=args['sticks'], fractions=ffs, snr=snr)
         except Exception as e:
             pass
-            # iflogger.warn('Exception simulating dwi signal: %s' % e)
+            # IFLOGGER.warn('Exception simulating dwi signal: %s' % e)
 
     return signal.tolist()