Merge remote-tracking branch 'upstream/master' into enh/circleci-neur…

…odocker

CHANGES

@@ -1,6 +1,7 @@
 Upcoming release (0.14.0)
 ================
 
+* FIX: Testing maintainance and improvements (https://github.com/nipy/nipype/pull/2252)
 * ENH: Add elapsed_time and final metric_value to ants.Registration (https://github.com/nipy/nipype/pull/1985)
 * ENH: Improve terminal_output feature (https://github.com/nipy/nipype/pull/2209)
 * ENH: Simple interface to FSL std2imgcoords (https://github.com/nipy/nipype/pull/2209, prev #1398)

conftest.py

@@ -0,0 +1,12 @@
+import pytest
+import numpy, os
+
+@pytest.fixture(autouse=True)
+def add_np(doctest_namespace):
+    doctest_namespace['np'] = numpy
+    doctest_namespace['os'] = os
+
+
+    filepath = os.path.dirname(os.path.realpath(__file__))
+    datadir = os.path.realpath(os.path.join(filepath, 'nipype/testing/data'))
+    doctest_namespace["datadir"] = datadir

nipype/algorithms/confounds.py

@@ -30,7 +30,7 @@
 from ..utils import NUMPY_MMAP
 from ..utils.misc import normalize_mc_params
 
-IFLOG = logging.getLogger('interface')
+IFLOGGER = logging.getLogger('interface')
 
 
 class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
@@ -286,7 +286,7 @@ def _run_interface(self, runtime):
                 tr = self.inputs.series_tr
 
             if self.inputs.normalize and tr is None:
-                IFLOG.warn('FD plot cannot be normalized if TR is not set')
+                IFLOGGER.warn('FD plot cannot be normalized if TR is not set')
 
             self._results['out_figure'] = op.abspath(self.inputs.out_figure)
             fig = plot_confound(fd_res, self.inputs.figsize, 'FD', units='mm',
@@ -601,8 +601,8 @@ def _process_masks(self, mask_images, timeseries=None):
             # save mask
             mask_file = os.path.abspath('mask_{:03d}.nii.gz'.format(i))
             out_image.to_filename(mask_file)
-            IFLOG.debug('tCompcor computed and saved mask of shape {} to '
-                        'mask_file {}'.format(mask.shape, mask_file))
+            IFLOGGER.debug('tCompcor computed and saved mask of shape %s to '
+                           'mask_file %s', str(mask.shape), mask_file)
             self._mask_files.append(mask_file)
             out_images.append(out_image)
         return out_images
@@ -919,7 +919,8 @@ def regress_poly(degree, data, remove_mean=True, axis=-1):
     :param int axis: numpy array axes along which regression is performed
 
     """
-    IFLOG.debug('Performing polynomial regression on data of shape ' + str(data.shape))
+    IFLOGGER.debug('Performing polynomial regression on data of shape %s',
+                   str(data.shape))
 
     datashape = data.shape
     timepoints = datashape[axis]
@@ -1147,7 +1148,7 @@ def _full_rank(X, cmax=1e15):
     c = smax / smin
     if c < cmax:
         return X, c
-    IFLOG.warn('Matrix is singular at working precision, regularizing...')
+    IFLOGGER.warn('Matrix is singular at working precision, regularizing...')
     lda = (smax - cmax * smin) / (cmax - 1)
     s = s + lda
     X = np.dot(U, np.dot(np.diag(s), V))

nipype/algorithms/misc.py

@@ -362,26 +362,23 @@ def _run_interface(self, runtime):
                 if isinstance(in_dict[key][0], np.ndarray):
                     saved_variables.append(key)
                 else:
-                    iflogger.info('One of the keys in the input file, {k}, is not a Numpy array'.format(k=key))
+                    iflogger.info('One of the keys in the input file, %s, is '
+                                  'not a Numpy array', key)
 
         if len(saved_variables) > 1:
-            iflogger.info(
-                '{N} variables found:'.format(N=len(saved_variables)))
+            iflogger.info('%i variables found:', len(saved_variables))
             iflogger.info(saved_variables)
             for variable in saved_variables:
-                iflogger.info(
-                    '...Converting {var} - type {ty} - to\
-                    CSV'.format(var=variable, ty=type(in_dict[variable]))
-                )
-                matlab2csv(
-                    in_dict[variable], variable, self.inputs.reshape_matrix)
+                iflogger.info('...Converting %s - type %s - to CSV',
+                              variable, type(in_dict[variable]))
+                matlab2csv(in_dict[variable], variable, self.inputs.reshape_matrix)
         elif len(saved_variables) == 1:
             _, name, _ = split_filename(self.inputs.in_file)
             variable = saved_variables[0]
-            iflogger.info('Single variable found {var}, type {ty}:'.format(
-                var=variable, ty=type(in_dict[variable])))
-            iflogger.info('...Converting {var} to CSV from {f}'.format(
-                var=variable, f=self.inputs.in_file))
+            iflogger.info('Single variable found %s, type %s:', variable,
+                          type(in_dict[variable]))
+            iflogger.info('...Converting %s to CSV from %s', variable,
+                          self.inputs.in_file)
             matlab2csv(in_dict[variable], name, self.inputs.reshape_matrix)
         else:
             iflogger.error('No values in the MATLAB file?!')
@@ -396,8 +393,8 @@ def _list_outputs(self):
                 if isinstance(in_dict[key][0], np.ndarray):
                     saved_variables.append(key)
                 else:
-                    iflogger.error('One of the keys in the input file, {k}, is\
-                                   not a Numpy array'.format(k=key))
+                    iflogger.error('One of the keys in the input file, %s, is '
+                                   'not a Numpy array', key)
 
         if len(saved_variables) > 1:
             outputs['csv_files'] = replaceext(saved_variables, '.csv')
@@ -555,28 +552,25 @@ def _run_interface(self, runtime):
             iflogger.info('Column headings have been provided:')
             headings = self.inputs.column_headings
         else:
-            iflogger.info(
-                'Column headings not provided! Pulled from input filenames:')
+            iflogger.info('Column headings not provided! Pulled from input filenames:')
             headings = remove_identical_paths(self.inputs.in_files)
 
         if isdefined(self.inputs.extra_field):
             if isdefined(self.inputs.extra_column_heading):
                 extraheading = self.inputs.extra_column_heading
-                iflogger.info('Extra column heading provided: {col}'.format(
-                    col=extraheading))
+                iflogger.info('Extra column heading provided: %s', extraheading)
             else:
                 extraheading = 'type'
-                iflogger.info(
-                    'Extra column heading was not defined. Using "type"')
+                iflogger.info('Extra column heading was not defined. Using "type"')
             headings.append(extraheading)
             extraheadingBool = True
 
         if len(self.inputs.in_files) == 1:
             iflogger.warn('Only one file input!')
 
         if isdefined(self.inputs.row_headings):
-            iflogger.info('Row headings have been provided. Adding "labels"\
-                          column header.')
+            iflogger.info('Row headings have been provided. Adding "labels"'
+                          'column header.')
             prefix = '"{p}","'.format(p=self.inputs.row_heading_title)
             csv_headings = prefix + '","'.join(itertools.chain(
                 headings)) + '"\n'
@@ -1310,7 +1304,7 @@ def merge_rois(in_files, in_idxs, in_ref,
     # to avoid memory errors
     if op.splitext(in_ref)[1] == '.gz':
         try:
-            iflogger.info('uncompress %i' % in_ref)
+            iflogger.info('uncompress %i', in_ref)
             sp.check_call(['gunzip', in_ref], stdout=sp.PIPE, shell=True)
             in_ref = op.splitext(in_ref)[0]
         except:

nipype/algorithms/modelgen.py

@@ -374,9 +374,9 @@ def _generate_standard_design(self, infolist, functional_runs=None,
                 for f in filename_to_list(sessinfo[i]['scans']):
                     shape = load(f, mmap=NUMPY_MMAP).shape
                     if len(shape) == 3 or shape[3] == 1:
-                        iflogger.warning(('You are using 3D instead of 4D '
-                                          'files. Are you sure this was '
-                                          'intended?'))
+                        iflogger.warning('You are using 3D instead of 4D '
+                                         'files. Are you sure this was '
+                                         'intended?')
                         numscans += 1
                     else:
                         numscans += shape[3]
@@ -686,7 +686,7 @@ def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
 
         if dt < 1:
             raise Exception('Time multiple less than 1 ms')
-        iflogger.info('Setting dt = %d ms\n' % dt)
+        iflogger.info('Setting dt = %d ms\n', dt)
         npts = int(np.ceil(total_time / dt))
         times = np.arange(0, total_time, dt) * 1e-3
         timeline = np.zeros((npts))
@@ -705,9 +705,9 @@ def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
             if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
                 response = np.convolve(boxcar, hrf)
                 reg_scale = 1.0 / response.max()
-                iflogger.info('response sum: %.4f max: %.4f' % (response.sum(),
-                                                                response.max()))
-            iflogger.info('reg_scale: %.4f' % reg_scale)
+                iflogger.info('response sum: %.4f max: %.4f', response.sum(),
+                              response.max())
+            iflogger.info('reg_scale: %.4f', reg_scale)
 
         for i, t in enumerate(onsets):
             idx = int(np.round(t / dt))

nipype/algorithms/tests/test_compcor.py

@@ -21,8 +21,7 @@ class TestCompCor():
     @pytest.fixture(autouse=True)
     def setup_class(self, tmpdir):
         # setup
-        self.temp_dir = str(tmpdir)
-        os.chdir(self.temp_dir)
+        tmpdir.chdir()
         noise = np.fromfunction(self.fake_noise_fun, self.fake_data.shape)
         self.realigned_file = utils.save_toy_nii(self.fake_data + noise,
                                                  self.filenames['functionalnii'])

nipype/algorithms/tests/test_confounds.py

@@ -20,7 +20,7 @@
 
 
 def test_fd(tmpdir):
-    tempdir = str(tmpdir)
+    tempdir = tmpdir.strpath
     ground_truth = np.loadtxt(example_data('fsl_motion_outliers_fd.txt'))
     fdisplacement = FramewiseDisplacement(in_file=example_data('fsl_mcflirt_movpar.txt'),
                                           out_file=tempdir + '/fd.txt',
@@ -43,7 +43,7 @@ def test_dvars(tmpdir):
                          in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
                          save_all=True,
                          intensity_normalization=0)
-    os.chdir(str(tmpdir))
+    tmpdir.chdir()
     res = dvars.run()
 
     dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
@@ -66,7 +66,8 @@ def test_dvars(tmpdir):
 
     assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
 
-def test_outliers(tmpdir):
+
+def test_outliers():
     np.random.seed(0)
     in_data = np.random.randn(100)
     in_data[0] += 10

nipype/algorithms/tests/test_errormap.py

@@ -11,7 +11,6 @@
 
 def test_errormap(tmpdir):
 
-    tempdir = str(tmpdir)
     # Single-Spectual
     # Make two fake 2*2*2 voxel volumes
     volume1 = np.array([[[2.0, 8.0], [1.0, 2.0]], [[1.0, 9.0], [0.0, 3.0]]])  # John von Neumann's birthday
@@ -22,15 +21,15 @@ def test_errormap(tmpdir):
     img2 = nb.Nifti1Image(volume2, np.eye(4))
     maskimg = nb.Nifti1Image(mask, np.eye(4))
 
-    nb.save(img1, os.path.join(tempdir, 'von.nii.gz'))
-    nb.save(img2, os.path.join(tempdir, 'alan.nii.gz'))
-    nb.save(maskimg, os.path.join(tempdir, 'mask.nii.gz'))
+    nb.save(img1, tmpdir.join('von.nii.gz').strpath)
+    nb.save(img2, tmpdir.join('alan.nii.gz').strpath)
+    nb.save(maskimg, tmpdir.join('mask.nii.gz').strpath)
 
     # Default metric
     errmap = ErrorMap()
-    errmap.inputs.in_tst = os.path.join(tempdir, 'von.nii.gz')
-    errmap.inputs.in_ref = os.path.join(tempdir, 'alan.nii.gz')
-    errmap.out_map = os.path.join(tempdir, 'out_map.nii.gz')
+    errmap.inputs.in_tst = tmpdir.join('von.nii.gz').strpath
+    errmap.inputs.in_ref = tmpdir.join('alan.nii.gz').strpath
+    errmap.out_map = tmpdir.join('out_map.nii.gz').strpath
     result = errmap.run()
     assert result.outputs.distance == 1.125
 
@@ -45,7 +44,7 @@ def test_errormap(tmpdir):
     assert result.outputs.distance == 0.875
 
     # Masked
-    errmap.inputs.mask = os.path.join(tempdir, 'mask.nii.gz')
+    errmap.inputs.mask = tmpdir.join('mask.nii.gz').strpath
     result = errmap.run()
     assert result.outputs.distance == 1.0
 
@@ -62,11 +61,11 @@ def test_errormap(tmpdir):
     msvolume2[:, :, :, 1] = volume1
     msimg2 = nb.Nifti1Image(msvolume2, np.eye(4))
 
-    nb.save(msimg1, os.path.join(tempdir, 'von-ray.nii.gz'))
-    nb.save(msimg2, os.path.join(tempdir, 'alan-ray.nii.gz'))
+    nb.save(msimg1, tmpdir.join('von-ray.nii.gz').strpath)
+    nb.save(msimg2, tmpdir.join('alan-ray.nii.gz').strpath)
 
-    errmap.inputs.in_tst = os.path.join(tempdir, 'von-ray.nii.gz')
-    errmap.inputs.in_ref = os.path.join(tempdir, 'alan-ray.nii.gz')
+    errmap.inputs.in_tst = tmpdir.join('von-ray.nii.gz').strpath
+    errmap.inputs.in_ref = tmpdir.join('alan-ray.nii.gz').strpath
     errmap.inputs.metric = 'sqeuclidean'
     result = errmap.run()
     assert result.outputs.distance == 5.5

nipype/algorithms/tests/test_mesh_ops.py

@@ -15,14 +15,13 @@
 
 @pytest.mark.skipif(VTKInfo.no_tvtk(), reason="tvtk is not installed")
 def test_ident_distances(tmpdir):
-    tempdir = str(tmpdir)
-    os.chdir(tempdir)
+    tmpdir.chdir() 
 
     in_surf = example_data('surf01.vtk')
     dist_ident = m.ComputeMeshWarp()
     dist_ident.inputs.surface1 = in_surf
     dist_ident.inputs.surface2 = in_surf
-    dist_ident.inputs.out_file = os.path.join(tempdir, 'distance.npy')
+    dist_ident.inputs.out_file = tmpdir.join('distance.npy')
     res = dist_ident.run()
     assert res.outputs.distance == 0.0
 
@@ -33,11 +32,11 @@ def test_ident_distances(tmpdir):
 
 @pytest.mark.skipif(VTKInfo.no_tvtk(), reason="tvtk is not installed")
 def test_trans_distances(tmpdir):
-    tempdir = str(tmpdir)
+    tempdir = tmpdir.strpath
     from ...interfaces.vtkbase import tvtk
 
     in_surf = example_data('surf01.vtk')
-    warped_surf = os.path.join(tempdir, 'warped.vtk')
+    warped_surf = tmpdir.join('warped.vtk')
 
     inc = np.array([0.7, 0.3, -0.2])
 
@@ -53,7 +52,7 @@ def test_trans_distances(tmpdir):
     dist = m.ComputeMeshWarp()
     dist.inputs.surface1 = in_surf
     dist.inputs.surface2 = warped_surf
-    dist.inputs.out_file = os.path.join(tempdir, 'distance.npy')
+    dist.inputs.out_file = tmpdir.join('distance.npy')
     res = dist.run()
     assert np.allclose(res.outputs.distance, np.linalg.norm(inc), 4)
     dist.inputs.weighting = 'area'
@@ -63,14 +62,14 @@ def test_trans_distances(tmpdir):
 
 @pytest.mark.skipif(VTKInfo.no_tvtk(), reason="tvtk is not installed")
 def test_warppoints(tmpdir):
-    os.chdir(str(tmpdir))
+    tmpdir.chdir()
 
     # TODO: include regression tests for when tvtk is installed
 
 
 @pytest.mark.skipif(VTKInfo.no_tvtk(), reason="tvtk is not installed")
 def test_meshwarpmaths(tmpdir):
-    os.chdir(str(tmpdir))
+    tmpdir.chdir()
 
     # TODO: include regression tests for when tvtk is installed
 

nipype/algorithms/tests/test_modelgen.py

@@ -17,9 +17,8 @@
 
 
 def test_modelgen1(tmpdir):
-    tempdir = str(tmpdir)
-    filename1 = os.path.join(tempdir, 'test1.nii')
-    filename2 = os.path.join(tempdir, 'test2.nii')
+    filename1 = tmpdir.join('test1.nii').strpath
+    filename2 = tmpdir.join('test2.nii').strpath
     Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename1)
     Nifti1Image(np.random.rand(10, 10, 10, 200), np.eye(4)).to_filename(filename2)
     s = SpecifyModel()
@@ -56,9 +55,8 @@ def test_modelgen1(tmpdir):
 
 
 def test_modelgen_spm_concat(tmpdir):
-    tempdir = str(tmpdir)
-    filename1 = os.path.join(tempdir, 'test1.nii')
-    filename2 = os.path.join(tempdir, 'test2.nii')
+    filename1 = tmpdir.join('test1.nii').strpath
+    filename2 = tmpdir.join('test2.nii').strpath
     Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename1)
     Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename2)
 
@@ -97,7 +95,7 @@ def test_modelgen_spm_concat(tmpdir):
     npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0]))
 
     # Test case for variable number of events in separate runs, sometimes unique.
-    filename3 = os.path.join(tempdir, 'test3.nii')
+    filename3 = tmpdir.join('test3.nii').strpath
     Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename3)
     s.inputs.functional_runs = [filename1, filename2, filename3]
     info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
@@ -122,9 +120,8 @@ def test_modelgen_spm_concat(tmpdir):
 
 
 def test_modelgen_sparse(tmpdir):
-    tempdir = str(tmpdir)
-    filename1 = os.path.join(tempdir, 'test1.nii')
-    filename2 = os.path.join(tempdir, 'test2.nii')
+    filename1 = tmpdir.join('test1.nii').strpath
+    filename2 = tmpdir.join('test2.nii').strpath
     Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename1)
     Nifti1Image(np.random.rand(10, 10, 10, 50), np.eye(4)).to_filename(filename2)
     s = SpecifySparseModel()