Merge branch 'master' into enh/ChainedNameSource

Conflicts:
	CHANGES

Author: oesteban

CHANGES

@@ -2,6 +2,10 @@ Next release
 ============
 
 * ENH: Inputs with name_source can be now chained in cascade (https://github.com/nipy/nipype/pull/938)
+* ENH: Improve JSON interfaces: default settings when reading and consistent output creation
+       when writing (https://github.com/nipy/nipype/pull/1047)
+* FIX: AddCSVRow problems when using infields (https://github.com/nipy/nipype/pull/1028)
+* FIX: Removed unused ANTS registration flag (https://github.com/nipy/nipype/pull/999)
 * FIX: Amend create_tbss_non_fa() workflow to match FSL's tbss_non_fa command. (https://github.com/nipy/nipype/pull/1033)
 * FIX: remove unused mandatory flag from spm normalize (https://github.com/nipy/nipype/pull/1048)
 * ENH: Update ANTSCorticalThickness interface (https://github.com/nipy/nipype/pull/1013)

nipype/algorithms/misc.py

@@ -34,7 +34,7 @@
 from ..interfaces.base import (BaseInterface, traits, TraitedSpec, File,
                                InputMultiPath, OutputMultiPath,
                                BaseInterfaceInputSpec, isdefined,
-                               DynamicTraitedSpec)
+                               DynamicTraitedSpec, Undefined)
 from nipype.utils.filemanip import fname_presuffix, split_filename
 iflogger = logging.getLogger('interface')
 
@@ -785,7 +785,8 @@ def _list_outputs(self):
 
 
 class AddCSVRowInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
-    in_file = traits.File(mandatory=True, desc='Input comma-separated value (CSV) files')
+    in_file = traits.File(mandatory=True,
+                          desc='Input comma-separated value (CSV) files')
     _outputs = traits.Dict(traits.Any, value={}, usedefault=True)
 
     def __setattr__(self, key, value):
@@ -804,13 +805,15 @@ class AddCSVRowOutputSpec(TraitedSpec):
 
 
 class AddCSVRow(BaseInterface):
+
     """Simple interface to add an extra row to a csv file
 
     .. note:: Requires `pandas <http://pandas.pydata.org/>`_
 
     .. warning:: Multi-platform thread-safe execution is possible with
-        `lockfile <https://pythonhosted.org/lockfile/lockfile.html>`_. Please recall that (1)
-        this module is alpha software; and (2) it should be installed for thread-safe writing.
+        `lockfile <https://pythonhosted.org/lockfile/lockfile.html>`_. Please
+        recall that (1) this module is alpha software; and (2) it should be
+        installed for thread-safe writing.
         If lockfile is not installed, then the interface is not thread-safe.
 
 
@@ -822,7 +825,7 @@ class AddCSVRow(BaseInterface):
     >>> addrow.inputs.in_file = 'scores.csv'
     >>> addrow.inputs.si = 0.74
     >>> addrow.inputs.di = 0.93
-    >>> addrow.subject_id = 'S400'
+    >>> addrow.inputs.subject_id = 'S400'
     >>> addrow.inputs.list_of_values = [ 0.4, 0.7, 0.3 ]
     >>> addrow.run() # doctest: +SKIP
     """
@@ -850,22 +853,26 @@ def _run_interface(self, runtime):
         try:
             import pandas as pd
         except ImportError:
-            raise ImportError('This interface requires pandas (http://pandas.pydata.org/) to run.')
+            raise ImportError(('This interface requires pandas '
+                               '(http://pandas.pydata.org/) to run.'))
 
         try:
             import lockfile as pl
             self._have_lock = True
         except ImportError:
-            import warnings
-            warnings.warn(('Python module lockfile was not found: AddCSVRow will not be thread-safe '
-                          'in multi-processor execution'))
+            from warnings import warn
+            warn(('Python module lockfile was not found: AddCSVRow will not be'
+                  ' thread-safe in multi-processor execution'))
 
         input_dict = {}
         for key, val in self.inputs._outputs.items():
             # expand lists to several columns
+            if key == 'trait_added' and val in self.inputs.copyable_trait_names():
+                continue
+
             if isinstance(val, list):
-                for i,v in enumerate(val):
-                    input_dict['%s_%d' % (key,i)]=v
+                for i, v in enumerate(val):
+                    input_dict['%s_%d' % (key, i)] = v
             else:
                 input_dict[key] = val
 
@@ -887,6 +894,13 @@ def _run_interface(self, runtime):
         if self._have_lock:
             self._lock.release()
 
+        # Using nipype.external.portalocker this might be something like:
+        # with pl.Lock(self.inputs.in_file, timeout=1) as fh:
+        #     if op.exists(fh):
+        #         formerdf = pd.read_csv(fh, index_col=0)
+        #         df = pd.concat([formerdf, df], ignore_index=True)
+        #         df.to_csv(fh)
+
         return runtime
 
     def _list_outputs(self):

nipype/interfaces/ants/registration.py

@@ -320,8 +320,6 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         low=0.0, high=1.0, value=1.0, argstr='%s', usedefault=True, desc="The Upper quantile to clip image ranges")
     winsorize_lower_quantile = traits.Range(
         low=0.0, high=1.0, value=0.0, argstr='%s', usedefault=True, desc="The Lower quantile to clip image ranges")
-    collapse_linear_transforms_to_fixed_image_header = traits.Bool(
-        argstr='%s', default=False, usedefault=True, desc='')
 
 
 class RegistrationOutputSpec(TraitedSpec):
@@ -376,29 +374,35 @@ class Registration(ANTSCommand):
 
     >>> reg1 = copy.deepcopy(reg)
     >>> reg1.inputs.winsorize_lower_quantile = 0.025
-    >>> reg1.inputs.collapse_linear_transforms_to_fixed_image_header = False
     >>> reg1.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 1.0 ]  --write-composite-transform 1'
     >>> reg1.run()  #doctest: +SKIP
 
     >>> reg2 = copy.deepcopy(reg)
     >>> reg2.inputs.winsorize_upper_quantile = 0.975
     >>> reg2.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 0.975 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 0.975 ]  --write-composite-transform 1'
 
     >>> reg3 = copy.deepcopy(reg)
     >>> reg3.inputs.winsorize_lower_quantile = 0.025
     >>> reg3.inputs.winsorize_upper_quantile = 0.975
     >>> reg3.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 0.975 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 0.975 ]  --write-composite-transform 1'
 
     >>> # Test collapse transforms flag
     >>> reg4 = copy.deepcopy(reg)
     >>> reg4.inputs.collapse_output_transforms = True
     >>> outputs = reg4._list_outputs()
     >>> print outputs #doctest: +ELLIPSIS
-    {'reverse_invert_flags': [True, False], 'inverse_composite_transform': ['.../nipype/testing/data/output_InverseComposite.h5'], 'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz', 'inverse_warped_image': <undefined>, 'forward_invert_flags': [False, False], 'reverse_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat', '.../nipype/testing/data/output_1InverseWarp.nii.gz'], 'composite_transform': ['.../nipype/testing/data/output_Composite.h5'], 'forward_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat', '.../nipype/testing/data/output_1Warp.nii.gz']}
-    >>> reg4.aggregate_outputs() #doctest: +SKIP
+    {'reverse_invert_flags': [], 'inverse_composite_transform': ['.../nipype/testing/data/output_InverseComposite.h5'], 'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz', 'inverse_warped_image': <undefined>, 'forward_invert_flags': [], 'reverse_transforms': [], 'composite_transform': ['.../nipype/testing/data/output_Composite.h5'], 'forward_transforms': []}
+    
+    >>> # Test collapse transforms flag
+    >>> reg4b = copy.deepcopy(reg4)
+    >>> reg4b.inputs.write_composite_transform = False
+    >>> outputs = reg4b._list_outputs()
+    >>> print outputs #doctest: +ELLIPSIS
+    {'reverse_invert_flags': [True, False], 'inverse_composite_transform': <undefined>, 'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz', 'inverse_warped_image': <undefined>, 'forward_invert_flags': [False, False], 'reverse_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat', '.../nipype/testing/data/output_1InverseWarp.nii.gz'], 'composite_transform': <undefined>, 'forward_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat', '.../nipype/testing/data/output_1Warp.nii.gz']}
+    >>> reg4b.aggregate_outputs() #doctest: +SKIP
 
     >>> # Test multiple metrics per stage
     >>> reg5 = copy.deepcopy(reg)
@@ -408,7 +412,7 @@ class Registration(ANTSCommand):
     >>> reg5.inputs.sampling_strategy = ['Random', None] # use default strategy in second stage
     >>> reg5.inputs.sampling_percentage = [0.05, [0.05, 0.10]]
     >>> reg5.cmdline
-    'antsRegistration --collapse-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -579,9 +583,11 @@ def _formatWinsorizeImageIntensities(self):
 
     def _formatCollapseLinearTransformsToFixedImageHeader(self):
         if self.inputs.collapse_linear_transforms_to_fixed_image_header:
-            return '--collapse-linear-transforms-to-fixed-image-header 1'
+            # return '--collapse-linear-transforms-to-fixed-image-header 1'
+            return ''
         else:
-            return '--collapse-linear-transforms-to-fixed-image-header 0'
+            # return '--collapse-linear-transforms-to-fixed-image-header 0'
+            return ''
 
     def _format_arg(self, opt, spec, val):
         if opt == 'fixed_image_mask':
@@ -710,7 +716,7 @@ def _list_outputs(self):
                 outputs[
                     'reverse_invert_flags'].insert(0, reverseInverseMode)
                 transformCount += 1
-        else:
+        elif not self.inputs.write_composite_transform:
             transformCount = 0
             isLinear = [any(self._linear_transform_names == t)
                         for t in self.inputs.transforms]

nipype/interfaces/ants/tests/test_auto_Registration.py

@@ -5,9 +5,6 @@
 def test_Registration_inputs():
     input_map = dict(args=dict(argstr='%s',
     ),
-    collapse_linear_transforms_to_fixed_image_header=dict(argstr='%s',
-    usedefault=True,
-    ),
     collapse_output_transforms=dict(argstr='--collapse-output-transforms %d',
     usedefault=True,
     ),