fix: mesh import to enable doc building

Author: satra

nipype/algorithms/mesh.py

@@ -12,46 +12,36 @@
 '''
 
 
-import os
-import os.path as op
-import warnings
-import nibabel as nb
 import numpy as np
 from scipy.spatial.distance import euclidean
-from nipype.utils.misc import package_check
-
-try:
-    package_check('tvtk')
-except Exception, e:
-    warnings.warn('tvtk or vtk not installed')
-else:
-    from tvtk.api import tvtk
-
 
 from .. import logging
 
 from ..interfaces.base import (BaseInterface, traits, TraitedSpec, File,
-                               InputMultiPath, OutputMultiPath,
-                               BaseInterfaceInputSpec, isdefined)
-from ..utils.filemanip import fname_presuffix, split_filename
+                               BaseInterfaceInputSpec)
 iflogger = logging.getLogger('interface')
 
 
 class P2PDistanceInputSpec(BaseInterfaceInputSpec):
     surface1 = File(exists=True, mandatory=True,
-                    desc="Reference surface (vtk format) to which compute distance.")
+                    desc=("Reference surface (vtk format) to which compute "
+                          "distance."))
     surface2 = File(exists=True, mandatory=True,
-                    desc="Test surface (vtk format) from which compute distance.")
-    weighting = traits.Enum("none", "surface", desc='""none": no weighting is performed\
-                            "surface": edge distance is weighted by the corresponding surface area',usedefault=True)
+                    desc=("Test surface (vtk format) from which compute "
+                          "distance."))
+    weighting = traits.Enum("none", "surface", usedefault=True,
+                            desc=('"none": no weighting is performed, '
+                                  '"surface": edge distance is weighted by the '
+                                  'corresponding surface area'))
 
 class P2PDistanceOutputSpec(TraitedSpec):
     distance = traits.Float(desc="computed distance")
 
 class P2PDistance(BaseInterface):
-    """
-    Calculates a point-to-point (p2p) distance between two corresponding meshes or contours.
-    Therefore, a point-to-point correspondence between nodes is required
+    """Calculates a point-to-point (p2p) distance between two corresponding
+    VTK-readable meshes or contours.
+
+    A point-to-point correspondence between nodes is required
 
     Example
     -------
@@ -74,6 +64,7 @@ def _triangle_area(self, A, B, C):
         return area
 
     def _run_interface(self, runtime):
+        from tvtk.api import tvtk
         r1 = tvtk.PolyDataReader( file_name=self.inputs.surface1 )
         r2 = tvtk.PolyDataReader( file_name=self.inputs.surface2 )
         vtk1 = r1.output
@@ -83,7 +74,7 @@ def _run_interface(self, runtime):
         assert( len(vtk1.points) == len(vtk2.points) )
         d = 0.0
         totalWeight = 0.0
-        
+
         points = vtk1.points
         faces = vtk1.polys.to_array().reshape(-1,4).astype(int)[:,1:]
 
@@ -110,4 +101,4 @@ def _list_outputs(self):
         outputs = self._outputs().get()
         outputs['distance'] = self._distance
         return outputs
-    
+

nipype/algorithms/misc.py

@@ -514,7 +514,7 @@ def _run_interface(self, runtime):
         ncomp = len(self.inputs.in_ref)
         assert( ncomp == len(self.inputs.in_tst) )
         weights = np.ones( shape=ncomp )
-    
+
         img_ref = np.array( [ nb.load( fname ).get_data() for fname in self.inputs.in_ref ] )
         img_tst = np.array( [ nb.load( fname ).get_data() for fname in self.inputs.in_tst ] )
 
@@ -558,11 +558,11 @@ def _run_interface(self, runtime):
         for w,ch in zip(weights,diff_im):
             ch[msk==0] = 0
             diff+= w* ch
-        
+
         nb.save(nb.Nifti1Image(diff, nb.load( self.inputs.in_ref[0]).get_affine(),
                 nb.load( self.inputs.in_ref[0]).get_header()), self.inputs.out_file )
 
- 
+
         return runtime
 
     def _list_outputs(self):

nipype/external/provcopy.py

@@ -355,7 +355,7 @@ def provn_representation(self):
 
     def json_representation(self):
         return {'$': self._uri, 'type': u'xsd:anyURI'}
-    
+
     def rdf_representation(self):
         return URIRef(self.get_uri())
 
@@ -757,7 +757,7 @@ def rdf(self, graph=None, subj=None):
                     obj = RDFLiteral(value)
                 graph.add((subj, pred, obj))
         return graph
-        
+
     def is_asserted(self):
         return self._asserted
 
@@ -772,7 +772,7 @@ def is_relation(self):
 class ProvElement(ProvRecord):
     def is_element(self):
         return True
-    
+
     def rdf(self, graph=None):
         if graph is None:
             graph = Graph()
@@ -1626,15 +1626,15 @@ def rdf(self, graph=None):
             # graph should not None here
             uri = self.get_identifier().rdf_representation()
             graph = Graph(graph.store, uri)
-        
+
         for prefix, namespace in self._namespaces.items():
             graph.bind(prefix, namespace.get_uri())
-        
+
         for record in self._records:
             if record.is_asserted():
                 record.rdf(graph)
         return graph
-    
+
     def get_provjson(self, **kw):
         """Return the `PROV-JSON <http://www.w3.org/Submission/prov-json/>`_ representation for the bundle/document.
 

nipype/interfaces/afni/__init__.py

@@ -1,7 +1,7 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 """The afni module provides classes for interfacing with the `AFNI
-<http://afni.nimh.nih.gov/afni>`_ command line tools.  
+<http://afni.nimh.nih.gov/afni>`_ command line tools.
 
 Top-level namespace for afni.
 """

nipype/interfaces/afni/preprocess.py

@@ -1849,7 +1849,7 @@ class AFNItoNIFTI(AFNICommand):
 
     see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAFNItoNIFTI.html>
     this can also convert 2D or 1D data, which you can numpy.squeeze() to remove extra dimensions
-    
+
     Examples
     ========
 

nipype/interfaces/camino/calib.py

@@ -20,18 +20,18 @@ class SFPICOCalibDataInputSpec(StdOutCommandLineInputSpec):
     scheme_file = File(exists=True, argstr='-schemefile %s', mandatory=True,
                        desc='Specifies the scheme file for the diffusion MRI data')
     info_file = File(desc='The name to be given to the information output filename.',
-                     argstr='-infooutputfile %s', mandatory=True, genfile=True, 
+                     argstr='-infooutputfile %s', mandatory=True, genfile=True,
                      hash_files=False) # Genfile and hash_files?
     trace = traits.Float(argstr='-trace %f', units='NA',
-                         desc='Trace of the diffusion tensor(s) used in the test function.')       
-    onedtfarange = traits.List(traits.Float, argstr='-onedtfarange %s', 
+                         desc='Trace of the diffusion tensor(s) used in the test function.')
+    onedtfarange = traits.List(traits.Float, argstr='-onedtfarange %s',
                                minlen=2, maxlen=2, units='NA',
                                desc=('Minimum and maximum FA for the single tensor '
                                      'synthetic data.'))
     onedtfastep = traits.Float(argstr='-onedtfastep %f', units='NA',
                                desc=('FA step size controlling how many steps there are '
                                      'between the minimum and maximum FA settings.'))
-    twodtfarange = traits.List(traits.Float, argstr='-twodtfarange %s', 
+    twodtfarange = traits.List(traits.Float, argstr='-twodtfarange %s',
                                minlen=2, maxlen=2, units='NA',
                                desc=('Minimum and maximum FA for the two tensor '
                                      'synthetic data. FA is varied for both tensors '
@@ -40,7 +40,7 @@ class SFPICOCalibDataInputSpec(StdOutCommandLineInputSpec):
                                desc=('FA step size controlling how many steps there are '
                                     'between the minimum and maximum FA settings '
                                     'for the two tensor cases.'))
-    twodtanglerange = traits.List(traits.Float, argstr='-twodtanglerange %s', 
+    twodtanglerange = traits.List(traits.Float, argstr='-twodtanglerange %s',
                                   minlen=2, maxlen=2, units='NA',
                                   desc=('Minimum and maximum crossing angles '
                                         'between the two fibres.'))
@@ -55,7 +55,7 @@ class SFPICOCalibDataInputSpec(StdOutCommandLineInputSpec):
                                 desc=('Mixing parameter step size for the two tensor cases. '
                                       'Specify how many mixing parameter increments to use.'))
     seed = traits.Float(argstr='-seed %f', units='NA',
-                        desc='Specifies the random seed to use for noise generation in simulation trials.')                             
+                        desc='Specifies the random seed to use for noise generation in simulation trials.')
 
 class SFPICOCalibDataOutputSpec(TraitedSpec):
     PICOCalib = File(exists=True, desc='Calibration dataset')
@@ -64,37 +64,37 @@ class SFPICOCalibDataOutputSpec(TraitedSpec):
 class SFPICOCalibData(StdOutCommandLine):
     """
     Generates Spherical Function PICo Calibration Data.
-    
-    SFPICOCalibData creates synthetic data for use with SFLUTGen. The 
-    synthetic data is generated using a mixture of gaussians, in the 
-    same way datasynth generates data.  Each voxel of data models a 
+
+    SFPICOCalibData creates synthetic data for use with SFLUTGen. The
+    synthetic data is generated using a mixture of gaussians, in the
+    same way datasynth generates data.  Each voxel of data models a
     slightly different fibre configuration (varying FA and fibre-
-    crossings) and undergoes a random rotation to help account for any 
-    directional bias in the chosen acquisition scheme.  A second file, 
+    crossings) and undergoes a random rotation to help account for any
+    directional bias in the chosen acquisition scheme.  A second file,
     which stores information about the datafile, is generated along with
     the datafile.
 
     Example 1
     ---------
     To create a calibration dataset using the default settings
-    
+
     >>> import nipype.interfaces.camino as cam
     >>> calib = cam.SFPICOCalibData()
     >>> calib.inputs.scheme_file = 'A.scheme'
     >>> calib.inputs.snr = 20
     >>> calib.inputs.info_file = 'PICO_calib.info'
-    >>> calib.run()           # doctest: +SKIP 
-    
-    The default settings create a large dataset (249,231 voxels), of 
-    which 3401 voxels contain a single fibre population per voxel and 
-    the rest of the voxels contain two fibre-populations. The amount of 
-    data produced can be varied by specifying the ranges and steps of 
+    >>> calib.run()           # doctest: +SKIP
+
+    The default settings create a large dataset (249,231 voxels), of
+    which 3401 voxels contain a single fibre population per voxel and
+    the rest of the voxels contain two fibre-populations. The amount of
+    data produced can be varied by specifying the ranges and steps of
     the parameters for both the one and two fibre datasets used.
-    
+
     Example 2
     ---------
     To create a custom calibration dataset
-    
+
     >>> import nipype.interfaces.camino as cam
     >>> calib = cam.SFPICOCalibData()
     >>> calib.inputs.scheme_file = 'A.scheme'
@@ -106,11 +106,11 @@ class SFPICOCalibData(StdOutCommandLine):
     >>> calib.inputs.twodtanglestep = 0.03925
     >>> calib.inputs.twodtmixmax = 0.8
     >>> calib.inputs.twodtmixstep = 0.1
-    >>> calib.run()              # doctest: +SKIP 
-    
+    >>> calib.run()              # doctest: +SKIP
+
     This would provide 76,313 voxels of synthetic data, where 3401 voxels
-    simulate the one fibre cases and 72,912 voxels simulate the various 
-    two fibre cases. However, care should be taken to ensure that enough 
+    simulate the one fibre cases and 72,912 voxels simulate the various
+    two fibre cases. However, care should be taken to ensure that enough
     data is generated for calculating the LUT.      # doctest: +SKIP
     """
     _cmd = 'sfpicocalibdata'
@@ -140,7 +140,7 @@ class SFLUTGenInputSpec(StdOutCommandLineInputSpec):
                                   '[outputstem]_oneFibreSurfaceCoeffs.Bdouble and '
                                   '[outputstem]_twoFibreSurfaceCoeffs.Bdouble'),
                             usedefault=True)
-    pdf = traits.Enum('bingham', 'watson', argstr='-pdf %s', 
+    pdf = traits.Enum('bingham', 'watson', argstr='-pdf %s',
                       desc=('Sets the distribution to use for the calibration. The default is the Bingham '
                             'distribution, which allows elliptical  probability  density  contours. '
                             'Currently supported options are: '
@@ -160,7 +160,7 @@ class SFLUTGenInputSpec(StdOutCommandLineInputSpec):
                                       'number  per  bin to get things running in quick tests, but the sta- '
                                       'tistics will not be reliable and for serious applications, you need  '
                                       'to increase the size of the calibration data set until the error goes.'))
-    directmap = traits.Bool(argstr='-directmap', 
+    directmap = traits.Bool(argstr='-directmap',
                             desc=('Use direct mapping between the eigenvalues and the distribution parameters '
                                   'instead of the log of the eigenvalues.'))
     order = traits.Int(argstr='-order %d', units='NA',
@@ -170,21 +170,21 @@ class SFLUTGenInputSpec(StdOutCommandLineInputSpec):
 class SFLUTGenOutputSpec(TraitedSpec):
     lut_one_fibre = File(exists=True, desc='PICo lut for one-fibre model')
     lut_two_fibres = File(exists=True, desc='PICo lut for two-fibre model')
-    
+
 class SFLUTGen(StdOutCommandLine):
     """
     Generates PICo lookup tables (LUT) for multi-fibre methods such as
     PASMRI and Q-Ball.
-    
+
     SFLUTGen creates the lookup tables for the generalized multi-fibre
     implementation of the PICo tractography algorithm.  The outputs of
     this utility are either surface or line coefficients up to a given
     order. The calibration can be performed for different distributions,
     such as the Bingham and Watson distributions.
-    
+
     This utility uses calibration data generated from SFPICOCalibData
     and peak information created by SFPeaks.
-    
+
     The utility outputs two lut's, *_oneFibreSurfaceCoeffs.Bdouble and
     *_twoFibreSurfaceCoeffs.Bdouble. Each of these files contains big-
     endian doubles as standard. The format of the output is:
@@ -194,9 +194,9 @@ class SFLUTGen(StdOutCommandLine):
       coefficient_2
       ...
       coefficient_N
-    In  the case of the Watson, there is a single set of coefficients, 
-    which are ordered: 
-      constant, x, x^2, ..., x^order. 
+    In  the case of the Watson, there is a single set of coefficients,
+    which are ordered:
+      constant, x, x^2, ..., x^order.
     In the case of the Bingham, there are two sets of coefficients (one
     for each surface), ordered so that:
       for j = 1 to order
@@ -207,12 +207,12 @@ class SFLUTGen(StdOutCommandLine):
     Example
     ---------
     To create a calibration dataset using the default settings
-    
+
     >>> import nipype.interfaces.camino as cam
     >>> lutgen = cam.SFLUTGen()
     >>> lutgen.inputs.in_file = 'QSH_peaks.Bdouble'
     >>> lutgen.inputs.info_file = 'PICO_calib.info'
-    >>> lutgen.run()        # doctest: +SKIP 
+    >>> lutgen.run()        # doctest: +SKIP
     """
     _cmd = 'sflutgen'
     input_spec=SFLUTGenInputSpec