Merge pull request #3 from nipy/master

up to date

Author: jdkent

doc/devel/testing_nipype.rst

@@ -89,7 +89,7 @@ To enable the tests depending on these data, just unpack the targz file and set
 variable to point to that folder.
 
 Xfail tests
-~~~~~~~~~~
+~~~~~~~~~~~
 
 Some tests are expect to fail until the code will be changed or for other reasons.
 

doc/documentation.rst

@@ -29,7 +29,7 @@ Previous versions:  `0.12.0 <http://nipype.readthedocs.io/en/0.12.0/>`_ `0.11.0
             :maxdepth: 2
 
             users/index
-            
+
          .. toctree::
             :maxdepth: 1
 

doc/users/config_file.rst

@@ -145,7 +145,7 @@ Execution
     crashfiles allow interactive debugging and rerunning of nodes, while text
     crashfiles allow portability across machines and shorter load time.
     (possible values: ``pklz`` and ``txt``; default value: ``pklz``)
-    
+
 Example
 ~~~~~~~
 

nipype/algorithms/confounds.py

@@ -6,11 +6,17 @@
 def test_ACompCor_inputs():
     input_map = dict(components_file=dict(usedefault=True,
     ),
-    header=dict(),
+    header_prefix=dict(),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
-    mask_file=dict(),
+    mask_files=dict(),
+    mask_index=dict(requires=['mask_files'],
+    xor=['merge_method'],
+    ),
+    merge_method=dict(requires=['mask_files'],
+    xor=['mask_index'],
+    ),
     num_components=dict(usedefault=True,
     ),
     realigned_file=dict(mandatory=True,

nipype/algorithms/tests/test_auto_ACompCor.py

@@ -6,11 +6,17 @@
 def test_TCompCor_inputs():
     input_map = dict(components_file=dict(usedefault=True,
     ),
-    header=dict(),
+    header_prefix=dict(),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
-    mask_file=dict(),
+    mask_files=dict(),
+    mask_index=dict(requires=['mask_files'],
+    xor=['merge_method'],
+    ),
+    merge_method=dict(requires=['mask_files'],
+    xor=['mask_index'],
+    ),
     num_components=dict(usedefault=True,
     ),
     percentile_threshold=dict(usedefault=True,
@@ -30,22 +36,8 @@ def test_TCompCor_inputs():
 
 
 def test_TCompCor_outputs():
-    output_map = dict(components_file=dict(usedefault=True,
-    ),
-    header=dict(),
-    high_variance_mask=dict(),
-    ignore_exception=dict(nohash=True,
-    usedefault=True,
-    ),
-    mask_file=dict(),
-    num_components=dict(usedefault=True,
-    ),
-    realigned_file=dict(mandatory=True,
-    ),
-    regress_poly_degree=dict(usedefault=True,
-    ),
-    use_regress_poly=dict(usedefault=True,
-    ),
+    output_map = dict(components_file=dict(),
+    high_variance_masks=dict(),
     )
     outputs = TCompCor.output_spec()
 

nipype/algorithms/tests/test_auto_TCompCor.py

@@ -8,7 +8,6 @@
 import pytest
 from ...testing import utils
 from ..confounds import CompCor, TCompCor, ACompCor
-from ...interfaces.base import Undefined
 
 
 class TestCompCor():
@@ -48,11 +47,13 @@ def test_compcor(self):
                                ['-0.1246655485', '-0.1235705610']]
 
         self.run_cc(CompCor(realigned_file=self.realigned_file,
-                            mask_files=self.mask_files),
+                            mask_files=self.mask_files,
+                            mask_index=0),
                             expected_components)
 
         self.run_cc(ACompCor(realigned_file=self.realigned_file,
                              mask_files=self.mask_files,
+                             mask_index=0,
                              components_file='acc_components_file'),
                              expected_components, 'aCompCor')
 
@@ -64,40 +65,44 @@ def test_tcompcor(self):
                                   ['0.4566907310', '0.6983205193'],
                                   ['-0.7132557407', '0.1340170559'],
                                   ['0.5022537643', '-0.5098322262'],
-                                  ['-0.1342351356', '0.1407855119']], 'tCompCor')
+                                  ['-0.1342351356', '0.1407855119']],
+                    'tCompCor')
 
     def test_tcompcor_no_percentile(self):
         ccinterface = TCompCor(realigned_file=self.realigned_file)
         ccinterface.run()
 
-        mask = nb.load('mask.nii').get_data()
+        mask = nb.load('mask_000.nii.gz').get_data()
         num_nonmasked_voxels = np.count_nonzero(mask)
         assert num_nonmasked_voxels == 1
 
     def test_compcor_no_regress_poly(self):
         self.run_cc(CompCor(realigned_file=self.realigned_file,
-                    mask_files=self.mask_files,
-                    use_regress_poly=False), [['0.4451946442', '-0.7683311482'],
-                                              ['-0.4285129505', '-0.0926034137'],
-                                              ['0.5721540256', '0.5608764842'],
-                                              ['-0.5367548139', '0.0059943226'],
-                                              ['-0.0520809054', '0.2940637551']])
+                            mask_files=self.mask_files,
+                            mask_index=0,
+                            use_regress_poly=False),
+                    [['0.4451946442', '-0.7683311482'],
+                     ['-0.4285129505', '-0.0926034137'],
+                     ['0.5721540256', '0.5608764842'],
+                     ['-0.5367548139', '0.0059943226'],
+                     ['-0.0520809054', '0.2940637551']])
 
     def test_tcompcor_asymmetric_dim(self):
         asymmetric_shape = (2, 3, 4, 5)
         asymmetric_data = utils.save_toy_nii(np.zeros(asymmetric_shape),
                                             'asymmetric.nii')
 
         TCompCor(realigned_file=asymmetric_data).run()
-        assert nb.load('mask.nii').get_data().shape == asymmetric_shape[:3]
+        assert nb.load('mask_000.nii.gz').get_data().shape == asymmetric_shape[:3]
 
     def test_compcor_bad_input_shapes(self):
         shape_less_than = (1, 2, 2, 5) # dim 0 is < dim 0 of self.mask_files (2)
         shape_more_than = (3, 3, 3, 5) # dim 0 is > dim 0 of self.mask_files (2)
 
         for data_shape in (shape_less_than, shape_more_than):
             data_file = utils.save_toy_nii(np.zeros(data_shape), 'temp.nii')
-            interface = CompCor(realigned_file=data_file, mask_files=self.mask_files)
+            interface = CompCor(realigned_file=data_file,
+                                mask_files=self.mask_files[0])
             with pytest.raises(ValueError, message="Dimension mismatch"): interface.run()
 
     def test_tcompcor_bad_input_dim(self):
@@ -112,19 +117,25 @@ def test_tcompcor_merge_intersect_masks(self):
                      mask_files=self.mask_files,
                      merge_method=method).run()
             if method == 'union':
-                assert np.array_equal(nb.load('mask.nii').get_data(),
+                assert np.array_equal(nb.load('mask_000.nii.gz').get_data(),
                                      ([[[0,0],[0,0]],[[0,0],[1,0]]]))
             if method == 'intersect':
-                assert np.array_equal(nb.load('mask.nii').get_data(),
+                assert np.array_equal(nb.load('mask_000.nii.gz').get_data(),
                                       ([[[0,0],[0,0]],[[0,1],[0,0]]]))
 
     def test_tcompcor_index_mask(self):
         TCompCor(realigned_file=self.realigned_file,
                  mask_files=self.mask_files,
                  mask_index=1).run()
-        assert np.array_equal(nb.load('mask.nii').get_data(),
+        assert np.array_equal(nb.load('mask_000.nii.gz').get_data(),
                             ([[[0,0],[0,0]],[[0,1],[0,0]]]))
 
+    def test_tcompcor_multi_mask_no_index(self):
+        interface = TCompCor(realigned_file=self.realigned_file,
+                             mask_files=self.mask_files)
+        with pytest.raises(ValueError, message='more than one mask file'):
+            interface.run()
+
     def run_cc(self, ccinterface, expected_components, expected_header='CompCor'):
         # run
         ccresult = ccinterface.run()
@@ -137,12 +148,15 @@ def run_cc(self, ccinterface, expected_components, expected_header='CompCor'):
         assert ccinterface.inputs.num_components == 6
 
         with open(ccresult.outputs.components_file, 'r') as components_file:
-            expected_n_components = min(ccinterface.inputs.num_components, self.fake_data.shape[3])
+            expected_n_components = min(ccinterface.inputs.num_components,
+                                        self.fake_data.shape[3])
 
             components_data = [line.split('\t') for line in components_file]
 
-            header = components_data.pop(0) # the first item will be '#', we can throw it out
-            expected_header = [expected_header + str(i) for i in range(expected_n_components)]
+            # the first item will be '#', we can throw it out
+            header = components_data.pop(0)
+            expected_header = [expected_header + '{:02d}'.format(i) for i in
+                               range(expected_n_components)]
             for i, heading in enumerate(header):
                 assert expected_header[i] in heading
 

nipype/algorithms/tests/test_compcor.py

@@ -20,4 +20,4 @@
 from .utils import (AFNItoNIFTI, Autobox, BrickStat, Calc, Copy,
                     Eval, FWHMx,
                     MaskTool, Merge, Notes, Refit, Resample, TCat, TStat, To3D,
-                    ZCutUp,)
+                    Unifize, ZCutUp,)

nipype/interfaces/afni/__init__.py

@@ -2417,7 +2417,7 @@ class QwarpPlusMinusOutputSpec(TraitedSpec):
 
 
 class QwarpPlusMinus(CommandLine):
-    """A version of 3dQwarp for performing field susceptibility correction 
+    """A version of 3dQwarp for performing field susceptibility correction
     using two images with opposing phase encoding directions.
 
     For complete details, see the `3dQwarp Documentation.
@@ -2434,7 +2434,7 @@ class QwarpPlusMinus(CommandLine):
     >>> qwarp.cmdline  # doctest: +ALLOW_UNICODE
     '3dQwarp -prefix Qwarp.nii.gz -plusminus -base sub-01_dir-RL_epi.nii.gz -nopadWARP -source sub-01_dir-LR_epi.nii.gz'
     >>> res = warp.run()  # doctest: +SKIP
-    
+
     """
     _cmd = '3dQwarp -prefix Qwarp.nii.gz -plusminus'
     input_spec = QwarpPlusMinusInputSpec

nipype/interfaces/afni/preprocess.py

@@ -695,7 +695,7 @@ class FWHMx(AFNICommandBase):
     _cmd = '3dFWHMx'
     input_spec = FWHMxInputSpec
     output_spec = FWHMxOutputSpec
-    
+
     references_ = [{'entry': BibTeX('@article{CoxReynoldsTaylor2016,'
                                     'author={R.W. Cox, R.C. Reynolds, and P.A. Taylor},'
                                     'title={AFNI and clustering: false positive rates redux},'
@@ -1237,6 +1237,108 @@ class To3D(AFNICommand):
     output_spec = AFNICommandOutputSpec
 
 
+class UnifizeInputSpec(AFNICommandInputSpec):
+    in_file = File(
+        desc='input file to 3dUnifize',
+        argstr='-input %s',
+        position=-1,
+        mandatory=True,
+        exists=True,
+        copyfile=False)
+    out_file = File(
+        desc='output image file name',
+        argstr='-prefix %s')
+    t2 = traits.Bool(
+        desc='Treat the input as if it were T2-weighted, rather than '
+             'T1-weighted. This processing is done simply by inverting '
+             'the image contrast, processing it as if that result were '
+             'T1-weighted, and then re-inverting the results '
+             'counts of voxel overlap, i.e., each voxel will contain the '
+             'number of masks that it is set in.',
+        argstr='-T2')
+    gm = traits.Bool(
+        desc='Also scale to unifize \'gray matter\' = lower intensity voxels '
+             '(to aid in registering images from different scanners).',
+        argstr='-GM')
+    urad = traits.Float(
+        desc='Sets the radius (in voxels) of the ball used for the sneaky '
+             'trick. Default value is 18.3, and should be changed '
+             'proportionally if the dataset voxel size differs significantly '
+             'from 1 mm.',
+        argstr='-Urad %s')
+    scale_file = File(
+        desc='output file name to save the scale factor used at each voxel ',
+        argstr='-ssave %s')
+    no_duplo = traits.Bool(
+        desc='Do NOT use the \'duplo down\' step; this can be useful for '
+             'lower resolution datasets.',
+        argstr='-noduplo')
+    epi = traits.Bool(
+        desc='Assume the input dataset is a T2 (or T2*) weighted EPI time '
+             'series. After computing the scaling, apply it to ALL volumes '
+             '(TRs) in the input dataset. That is, a given voxel will be '
+             'scaled by the same factor at each TR. '
+             'This option also implies \'-noduplo\' and \'-T2\'.'
+             'This option turns off \'-GM\' if you turned it on.',
+        argstr='-EPI',
+        requires=['no_duplo', 't2'],
+        xor=['gm'])
+
+
+class UnifizeOutputSpec(TraitedSpec):
+    scale_file = File(desc='scale factor file')
+    out_file = File(desc='unifized file', exists=True)
+
+
+class Unifize(AFNICommand):
+    """3dUnifize - for uniformizing image intensity
+
+    * The input dataset is supposed to be a T1-weighted volume,
+      possibly already skull-stripped (e.g., via 3dSkullStrip).
+      However, this program can be a useful step to take BEFORE
+      3dSkullStrip, since the latter program can fail if the input
+      volume is strongly shaded -- 3dUnifize will (mostly) remove
+      such shading artifacts.
+
+    * The output dataset has the white matter (WM) intensity approximately
+      uniformized across space, and scaled to peak at about 1000.
+
+    * The output dataset is always stored in float format!
+
+    * If the input dataset has more than 1 sub-brick, only sub-brick
+      #0 will be processed!
+
+    * Want to correct EPI datasets for nonuniformity?
+      You can try the new and experimental [Mar 2017] '-EPI' option.
+
+    * The principal motive for this program is for use in an image
+      registration script, and it may or may not be useful otherwise.
+
+    * This program replaces the older (and very different) 3dUniformize,
+      which is no longer maintained and may sublimate at any moment.
+      (In other words, we do not recommend the use of 3dUniformize.)
+
+    For complete details, see the `3dUnifize Documentation.
+    <https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dUnifize.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> unifize = afni.Unifize()
+    >>> unifize.inputs.in_file = 'structural.nii'
+    >>> unifize.inputs.out_file = 'structural_unifized.nii'
+    >>> unifize.cmdline  # doctest: +ALLOW_UNICODE
+    '3dUnifize -prefix structural_unifized.nii -input structural.nii'
+    >>> res = unifize.run()  # doctest: +SKIP
+
+    """
+
+    _cmd = '3dUnifize'
+    input_spec = UnifizeInputSpec
+    output_spec = UnifizeOutputSpec
+
+
 class ZCutUpInputSpec(AFNICommandInputSpec):
     in_file = File(
         desc='input file to 3dZcutup',