fix: many miscellaneous fixes - mostly styles - but also for doc building the ipython extension has moved to ipython

Author: satra

doc/conf.py

@@ -39,7 +39,7 @@
               'numpy_ext.numpydoc',
               'matplotlib.sphinxext.plot_directive',
               'matplotlib.sphinxext.only_directives',
-              'matplotlib.sphinxext.ipython_directive',
+              'IPython.sphinxext.ipython_directive',
               'ipython_console_highlighting'
               ]
 

nipype/interfaces/afni/preprocess.py

@@ -1935,7 +1935,7 @@ def _overload_extension(self, value):
 
     def _gen_filename(self, name):
         return os.path.abspath(super(AFNItoNIFTI, self)._gen_filename(name))
-        
+
 class EvalInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 1deval',
                      argstr='-a %s', position=0, mandatory=True, exists=True)
@@ -1955,15 +1955,15 @@ class EvalInputSpec(AFNICommandInputSpec):
                           requires=['start_idx'])
     single_idx = traits.Int(desc='volume index for in_file_a')
     other = File(desc='other options', argstr='')
-    
+
 class Eval(AFNICommand):
     """Evaluates an expression that may include columns of data from one or more text files
 
     see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>
-    
+
     Examples
     ========
-    
+
     >>> from nipype.interfaces import afni as afni
     >>> eval = afni.Eval()
     >>> eval.inputs.in_file_a = 'seed.1D'
@@ -1996,7 +1996,7 @@ def _parse_inputs(self, skip=None):
         """
         return super(Eval, self)._parse_inputs(
             skip=('start_idx', 'stop_idx', 'out1D', 'other'))
-            
+
 class MeansInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 3dMean',
         argstr='%s',

nipype/interfaces/afni/svm.py

@@ -24,28 +24,28 @@
 
 warn = warnings.warn
 warnings.filterwarnings('always', category=UserWarning)
-    
+
 class SVMTrainInputSpec(AFNICommandInputSpec):
     #training options
-    ttype = traits.Str(desc='tname: classification or regression', 
+    ttype = traits.Str(desc='tname: classification or regression',
                 argstr='-type %s',
                 mandatory=True)
     in_file = File(desc='A 3D+t AFNI brik dataset to be used for training.',
                 argstr='-trainvol %s',
                 mandatory=True,
                 exists=True,
                 copyfile=False)
-    out_file = File(name_template="%s_vectors", 
+    out_file = File(name_template="%s_vectors",
                 desc='output sum of weighted linear support vectors file name',
                 argstr='-bucket %s',
-                suffix='_bucket', 
+                suffix='_bucket',
                 name_source="in_file")
     model = File(name_template="%s_model",
                 desc='basename for the brik containing the SVM model',
                 argstr='-model %s',
-                suffix='_model', 
-                name_source="in_file")    
-    alphas = File(name_template="%s_alphas", 
+                suffix='_model',
+                name_source="in_file")
+    alphas = File(name_template="%s_alphas",
                 desc='output alphas file name',
                 argstr='-alpha %s',
                 suffix='_alphas',
@@ -62,20 +62,20 @@ class SVMTrainInputSpec(AFNICommandInputSpec):
                 exists=True)
     censor = File(desc='.1D censor file that allows the user to ignore certain samples in the training data.',
                 argstr='-censor %s',
-                exists=True)   
+                exists=True)
     kernel = traits.Str(desc='string specifying type of kernel function:linear, polynomial, rbf, sigmoid',
                 argstr='-kernel %s')
     max_iterations = traits.Int(desc='Specify the maximum number of iterations for the optimization.',
                 argstr='-max_iterations %d')
     w_out = traits.Bool(desc='output sum of weighted linear support vectors',
                 argstr='-wout')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-                
+
 class SVMTrainOutputSpec(TraitedSpec):
     out_file = File(desc='sum of weighted linear support vectors file name')
     model = File(desc='brik containing the SVM model file name')
     alphas = File(desc='output alphas file name')
-                
+
 class SVMTrain(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Train Only
@@ -129,7 +129,7 @@ class SVMTestInputSpec(AFNICommandInputSpec):
     multiclass = traits.Bool(desc='Specifies multiclass algorithm for classification',
                 argstr='-multiclass %s')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-    
+
 class SVMTest(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Test Only

nipype/interfaces/camino/connectivity.py

@@ -37,7 +37,7 @@ class ConmatInputSpec(CommandLineInputSpec):
                              desc=("Tract statistic to use. See TractStats for other options."),
                              requires=['scalar_file'],xor=['tract_prop'])
 
-    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s', 
+    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s',
                              units='NA', xor=['tract_stat'],
                              desc=('Tract property average to compute in the connectivity matrix. '
                                    'See TractStats for details.'))

nipype/interfaces/dcmstack.py

@@ -85,7 +85,7 @@ class DcmStackInputSpec(NiftiGeneratorBaseInputSpec):
                                   "any default exclude filters")
     include_regexes = traits.List(desc="Meta data to include, overriding any "
                                   "exclude filters")
-    force_read = traits.Bool(True, usedefault=True, 
+    force_read = traits.Bool(True, usedefault=True,
                              desc=('Force reading files without DICM marker'))
 
 

nipype/interfaces/fsl/epi.py

@@ -577,11 +577,11 @@ class EpiRegInputSpec(FSLCommandInputSpec):
                         (use if fmap already registered)')
     no_clean = traits.Bool(False, argstr='--noclean',
                         desc='do not clean up intermediate files')
-    
+
 
 class EpiRegOutputSpec(TraitedSpec):
     out_file = File(exists=True,
-                    desc='unwarped and coregistered epi input')     
+                    desc='unwarped and coregistered epi input')
     out_1vol = File(exists=True,
                           desc='unwarped and coregistered single volume')
     fmap2str_mat = File(exists=True,
@@ -607,7 +607,7 @@ class EpiRegOutputSpec(TraitedSpec):
 class EpiReg(FSLCommand):
     """
 
-    Runs FSL epi_reg script for simultaneous coregistration and fieldmap 
+    Runs FSL epi_reg script for simultaneous coregistration and fieldmap
     unwarping.
 
     Examples
@@ -629,7 +629,7 @@ class EpiReg(FSLCommand):
 --fmapmag=fieldmap_mag.nii --fmapmagbrain=fieldmap_mag_brain.nii --pedir=y \
 --epi=epi.nii --t1=T1.nii --t1brain=T1_brain.nii --out=epi2struct'
     >>> epireg.run() # doctest: +SKIP
-    
+
     """
     _cmd = 'epi_reg'
     input_spec = EpiRegInputSpec

nipype/interfaces/fsl/tests/test_auto_BEDPOSTX.py

@@ -3,39 +3,85 @@
 from nipype.interfaces.fsl.dti import BEDPOSTX
 
 def test_BEDPOSTX_inputs():
-    input_map = dict(args=dict(argstr='%s',
+    input_map = dict(all_ard=dict(argstr='--allard',
+    xor=('no_ard', 'all_ard'),
     ),
-    bpx_directory=dict(argstr='%s',
-    usedefault=True,
+    args=dict(argstr='%s',
+    ),
+    burn_in=dict(argstr='--burnin=%d',
+    ),
+    burn_in_no_ard=dict(argstr='--burninnoard=%d',
     ),
-    burn_period=dict(argstr='-b %d',
+    bvals=dict(argstr='--bvals=%s',
+    mandatory=True,
     ),
-    bvals=dict(mandatory=True,
+    bvecs=dict(argstr='--bvecs=%s',
+    mandatory=True,
     ),
-    bvecs=dict(mandatory=True,
+    cnlinear=dict(argstr='--cnonlinear',
+    xor=('no_spat', 'non_linear', 'cnlinear'),
     ),
-    dwi=dict(mandatory=True,
+    dwi=dict(argstr='--data=%s',
+    mandatory=True,
     ),
     environ=dict(nohash=True,
     usedefault=True,
     ),
-    fibres=dict(argstr='-n %d',
+    f0_ard=dict(argstr='--f0 --ardf0',
+    xor=['f0_noard', 'f0_ard', 'all_ard'],
+    ),
+    f0_noard=dict(argstr='--f0',
+    xor=['f0_noard', 'f0_ard'],
+    ),
+    force_dir=dict(argstr='--forcedir',
+    usedefault=True,
+    ),
+    fudge=dict(argstr='--fudge=%d',
+    ),
+    gradnonlin=dict(argstr='-g',
     ),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
-    jumps=dict(argstr='-j %d',
+    logdir=dict(argstr='--logdir=%s',
+    usedefault=True,
+    ),
+    mask=dict(argstr='--mask=%s',
+    mandatory=True,
     ),
-    mask=dict(mandatory=True,
+    model=dict(argstr='--model=%d',
+    ),
+    n_fibres=dict(argstr='--nfibres=%d',
+    ),
+    n_jumps=dict(argstr='--njumps=%d',
+    ),
+    no_ard=dict(argstr='--noard',
+    xor=('no_ard', 'all_ard'),
+    ),
+    no_spat=dict(argstr='--nospat',
+    xor=('no_spat', 'non_linear', 'cnlinear'),
+    ),
+    non_linear=dict(argstr='--nonlinear',
+    xor=('no_spat', 'non_linear', 'cnlinear'),
+    ),
+    out_dir=dict(argstr='%s',
+    mandatory=True,
+    position=1,
+    usedefault=True,
     ),
     output_type=dict(),
-    sampling=dict(argstr='-s %d',
+    rician=dict(argstr='--rician',
+    ),
+    sample_every=dict(argstr='--sampleevery=%d',
+    ),
+    seed=dict(argstr='--seed=%d',
     ),
     terminal_output=dict(mandatory=True,
     nohash=True,
     ),
-    weight=dict(argstr='-w %.2f',
+    update_proposal_every=dict(argstr='--updateproposalevery=%d',
     ),
+    use_gpu=dict(),
     )
     inputs = BEDPOSTX.input_spec()
 
@@ -44,15 +90,23 @@ def test_BEDPOSTX_inputs():
             yield assert_equal, getattr(inputs.traits()[key], metakey), value
 
 def test_BEDPOSTX_outputs():
-    output_map = dict(bpx_out_directory=dict(),
+    output_map = dict(d_stdsamples=dict(),
+    dsamples=dict(),
     dyads=dict(),
+    dyads_disp=dict(),
+    fsamples=dict(),
+    mean_S0samples=dict(),
+    mean_d_stdsamples=dict(),
+    mean_dsamples=dict(),
     mean_fsamples=dict(),
     mean_phsamples=dict(),
+    mean_tausamples=dict(),
     mean_thsamples=dict(),
     merged_fsamples=dict(),
     merged_phsamples=dict(),
     merged_thsamples=dict(),
-    xfms_directory=dict(),
+    phsamples=dict(),
+    thsamples=dict(),
     )
     outputs = BEDPOSTX.output_spec()
 

nipype/interfaces/fsl/tests/test_auto_XFibres.py

@@ -18,12 +18,21 @@ def test_XFibres_inputs():
     bvecs=dict(argstr='--bvecs=%s',
     mandatory=True,
     ),
+    cnlinear=dict(argstr='--cnonlinear',
+    xor=('no_spat', 'non_linear', 'cnlinear'),
+    ),
     dwi=dict(argstr='--data=%s',
     mandatory=True,
     ),
     environ=dict(nohash=True,
     usedefault=True,
     ),
+    f0_ard=dict(argstr='--f0 --ardf0',
+    xor=['f0_noard', 'f0_ard', 'all_ard'],
+    ),
+    f0_noard=dict(argstr='--f0',
+    xor=['f0_noard', 'f0_ard'],
+    ),
     force_dir=dict(argstr='--forcedir',
     usedefault=True,
     ),
@@ -50,12 +59,14 @@ def test_XFibres_inputs():
     xor=('no_ard', 'all_ard'),
     ),
     no_spat=dict(argstr='--nospat',
-    xor=('no_spat', 'non_linear'),
+    xor=('no_spat', 'non_linear', 'cnlinear'),
     ),
     non_linear=dict(argstr='--nonlinear',
-    xor=('no_spat', 'non_linear'),
+    xor=('no_spat', 'non_linear', 'cnlinear'),
     ),
     output_type=dict(),
+    rician=dict(argstr='--rician',
+    ),
     sample_every=dict(argstr='--sampleevery=%d',
     ),
     seed=dict(argstr='--seed=%d',
@@ -73,11 +84,15 @@ def test_XFibres_inputs():
             yield assert_equal, getattr(inputs.traits()[key], metakey), value
 
 def test_XFibres_outputs():
-    output_map = dict(dyads=dict(),
+    output_map = dict(d_stdsamples=dict(),
+    dsamples=dict(),
+    dyads=dict(),
     fsamples=dict(),
     mean_S0samples=dict(),
+    mean_d_stdsamples=dict(),
     mean_dsamples=dict(),
     mean_fsamples=dict(),
+    mean_tausamples=dict(),
     phsamples=dict(),
     thsamples=dict(),
     )