Merge pull request #647 from satra/fix/cherrypicked

Fix/cherrypicked

Author: satra

examples/smri_ants_registration.py

@@ -59,27 +59,29 @@
 reg.inputs.output_transform_prefix = 'thisTransform'
 reg.inputs.output_warped_image = 'INTERNAL_WARPED.nii.gz'
 
+reg.inputs.output_transform_prefix = "output_"
 reg.inputs.transforms = ['Translation', 'Rigid', 'Affine', 'SyN']
-reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.3, 3.0, 0.0)]
-reg.inputs.number_of_iterations = [[10000, 0, 0], [10000, 0, 0], [10000, 0, 0], [10, 0, 0]]
+reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.2, 3.0, 0.0)]
+reg.inputs.number_of_iterations = ([[10000, 111110, 11110]]*3 +
+                                    [[100, 50, 30]])
 reg.inputs.dimension = 3
 reg.inputs.write_composite_transform = True
-reg.inputs.collapse_output_transforms = True
-reg.inputs.metric = ['Mattes']*4
-reg.inputs.metric_weight = [1]*4 # Default (value ignored currently by ANTs)
-reg.inputs.radius_or_number_of_bins = [32]*4
-reg.inputs.sampling_strategy = ['Regular']*3 + [None]
-reg.inputs.sampling_percentage = [0.1]*3 + [None]
-reg.inputs.convergence_threshold = [1.e-8]*4
-reg.inputs.convergence_window_size = [20]*4
-reg.inputs.smoothing_sigmas = [[4,2,1]]*3 + [[2,1,0]]
-reg.inputs.sigma_units = ['vox']*4
-reg.inputs.shrink_factors = [[6,4,2]]*3 + [[4,2,1]]
-reg.inputs.use_estimate_learning_rate_once = [True, True, True, True]
-reg.inputs.use_histogram_matching = [False]*3 + [True] # This is the default
+reg.inputs.collapse_output_transforms = False
+reg.inputs.metric = ['Mattes'] * 3 + [['Mattes', 'CC']]
+reg.inputs.metric_weight = [1] * 3 + [[0.5, 0.5]]
+reg.inputs.radius_or_number_of_bins = [32] * 3 + [[32, 4]]
+reg.inputs.sampling_strategy = ['Regular'] * 3 + [[None, None]]
+reg.inputs.sampling_percentage = [0.3] * 3 + [[None, None]]
+reg.inputs.convergence_threshold = [1.e-8] * 3 + [-0.01]
+reg.inputs.convergence_window_size = [20] * 3 + [5]
+reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 3 + [[1, 0.5, 0]]
+reg.inputs.sigma_units = ['vox'] * 4
+reg.inputs.shrink_factors = [[6, 4, 2]] + [[3, 2, 1]]*2 + [[4, 2, 1]]
+reg.inputs.use_estimate_learning_rate_once = [True] * 4
+reg.inputs.use_histogram_matching = [False] * 3 + [True]
 reg.inputs.initial_moving_transform_com = True
-reg.inputs.output_warped_image = True
-reg.cmdline
+
+print reg.cmdline
 
 
 """

nipype/interfaces/ants/registration.py

@@ -283,7 +283,7 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
                                                                   traits.Float())))
     # Convergence flags
     number_of_iterations = traits.List(traits.List(traits.Int()))
-    smoothing_sigmas = traits.List(traits.List(traits.Int()), mandatory=True)
+    smoothing_sigmas = traits.List(traits.List(traits.Float()), mandatory=True)
     sigma_units = traits.List(traits.Enum('mm', 'vox'),
                               requires=['smoothing_sigmas'],
                               desc="units for smoothing sigmas", mandatory=True)
@@ -356,37 +356,37 @@ class Registration(ANTSCommand):
     >>> 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 1x0vox --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 2x1x0vox --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-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'
     >>> 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 1x0vox --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 2x1x0vox --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-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'
 
     >>> 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 1x0vox --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 2x1x0vox --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-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'
 
-    # Test collapse transforms flag
+    >>> # 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
 
-    # Test multiple metrics per stage
+    >>> # Test multiple metrics per stage
     >>> reg5 = copy.deepcopy(reg)
     >>> reg5.inputs.metric = ['CC', ['CC', 'Mattes']]
     >>> reg5.inputs.metric_weight = [1, [.5]*2]
     >>> reg5.inputs.radius_or_number_of_bins = [4, [32]*2]
     >>> 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 1x0vox --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, Dense, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, Dense, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2x1x0vox --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-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, Dense, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, Dense, 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 = 'Dense'
     """The default sampling stratey argument."""

nipype/interfaces/ants/resampling.py

@@ -208,9 +208,16 @@ def _list_outputs(self):
 
 
 class ApplyTransformsInputSpec(ANTSCommandInputSpec):
-    dimension = traits.Enum(
-        3, 2, argstr='--dimensionality %d', usedefault=True,
-        desc='image dimension (2 or 3)')
+    dimension = traits.Enum(2, 3, 4, argstr='--dimensionality %d',
+                            desc=('This option forces the image to be treated '
+                                  'as a specified-dimensional image. If not '
+                                  'specified, antsWarp tries to infer the '
+                                  'dimensionality from the input image.'))
+    input_image_type = traits.Enum(0, 1, 2, 3,
+                                   argstr='--input-image-type %d',
+                                   desc=('Option specifying the input image '
+                                         'type of scalar (default), vector, '
+                                         'tensor, or time series.'))
     input_image = File(argstr='--input %s', mandatory=True,
                        desc=('image to apply transformation to (generally a '
                               'coregistered functional)'),

nipype/interfaces/base.py

@@ -701,18 +701,28 @@ def safe_encode(x):
         return pm.Literal(int(x), pm.XSD['integer'])
     if isinstance(x, (float,)):
         return pm.Literal(x, pm.XSD['float'])
+    if isinstance(x, dict):
+        outdict = {}
+        for key, value in x.items():
+            encoded_value = safe_encode(value)
+            if isinstance(encoded_value, (pm.Literal,)):
+                outdict[key] = encoded_value.json_representation()
+            else:
+                outdict[key] = encoded_value
+        return pm.Literal(json.dumps(outdict), pm.XSD['string'])
+    if isinstance(x, list):
+        outlist = []
+        for value in x:
+            encoded_value = safe_encode(value)
+            if isinstance(encoded_value, (pm.Literal,)):
+                outlist.append(encoded_value.json_representation())
+            else:
+                outlist.append(encoded_value)
+        return pm.Literal(json.dumps(outlist), pm.XSD['string'])
     try:
-        if isinstance(x, dict):
-            outdict = {}
-            for key, value in x.items():
-                outdict[key] = safe_encode(value)
-            return pm.Literal(json.dumps(outdict), pm.XSD['string'])
-        if isinstance(x, list):
-            outlist = [safe_encode(value) for value in x]
-            return pm.Literal(json.dumps(outlist), pm.XSD['string'])
-        return pm.Literal(dumps(x),
-                          nipype['pickle'])
-    except TypeError:
+        return pm.Literal(dumps(x), nipype['pickle'])
+    except TypeError, e:
+        iflogger.info(e)
         return pm.Literal("Could not encode", pm.XSD['string'])
 
 

nipype/interfaces/freesurfer/preprocess.py

@@ -784,6 +784,8 @@ class ApplyVolTransformInputSpec(FSTraitedSpec):
                       desc='Output template volume', mandatory=True)
     tal = traits.Bool(argstr='--tal', xor=_targ_xor, mandatory=True,
                       desc='map to a sub FOV of MNI305 (with --reg only)')
+    tal_resolution = traits.Float(argstr="--talres %.10f",
+                                  desc="Resolution to sample when using tal")
     fs_target = traits.Bool(argstr='--fstarg', xor=_targ_xor, mandatory=True,
                          requires=['reg_file'],
                          desc='use orig.mgz from subject in regfile as target')
@@ -805,10 +807,30 @@ class ApplyVolTransformInputSpec(FSTraitedSpec):
                    desc='set matrix = identity and use subject for any templates')
     inverse = traits.Bool(desc='sample from target to source',
                           argstr='--inv')
-    interp = traits.Enum('trilin', 'nearest', argstr='--interp %s',
+    interp = traits.Enum('trilin', 'nearest', 'cubic', argstr='--interp %s',
                          desc='Interpolation method (<trilin> or nearest)')
     no_resample = traits.Bool(desc='Do not resample; just change vox2ras matrix',
                               argstr='--no-resample')
+    m3z_file = File(argstr="--m3z %s",
+                    desc=('This is the morph to be applied to the volume. '
+                          'Unless the morph is in mri/transforms (eg.: for '
+                          'talairach.m3z computed by reconall), you will need '
+                          'to specify the full path to this morph and use the '
+                          '--noDefM3zPath flag.'))
+    no_ded_m3z_path = traits.Bool(argstr="--noDefM3zPath",
+                                  requires=['m3z_file'],
+                                  desc=('To be used with the m3z flag. '
+                                        'Instructs the code not to look for the'
+                                        'm3z morph in the default location '
+                                        '(SUBJECTS_DIR/subj/mri/transforms), '
+                                        'but instead just use the path '
+                                        'indicated in --m3z.'))
+
+    invert_morph = traits.Bool(argstr="--inv-morph",
+                               requires=['m3z_file'],
+                               desc=('Compute and use the inverse of the '
+                                     'non-linear morph to resample the input '
+                                     'volume. To be used by --m3z.'))
 
 
 class ApplyVolTransformOutputSpec(TraitedSpec):
@@ -853,7 +875,7 @@ def _get_outfile(self):
 
     def _list_outputs(self):
         outputs = self.output_spec().get()
-        outputs['transformed_file'] = self._get_outfile()
+        outputs['transformed_file'] = os.path.abspath(self._get_outfile())
         return outputs
 
     def _gen_filename(self, name):
@@ -1192,11 +1214,3 @@ def _gen_filename(self, name):
         if name == "out_file":
             return self._list_outputs()["out_file"]
         return None
-
-'''
-interfaces to do:
-
-mri_vol2surf
-mri_surf2vol
-mri_surf2surf
-'''

nipype/pipeline/engine.py

@@ -1211,7 +1211,7 @@ def hash_exists(self, updatehash=False):
         outdir = self.output_dir()
         hashfiles = glob(os.path.join(outdir, '_0x*.json'))
         if len(hashfiles) > 1:
-            warn('Removing multiple hashfiles and forcing node to rerun')
+            logger.info('Removing multiple hashfiles and forcing node to rerun')
             for hashfile in hashfiles:
                 os.unlink(hashfile)
         hashfile = os.path.join(outdir, '_0x%s.json' % hashvalue)