pytest cleaning

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/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()

nipype/algorithms/tests/test_moments.py

@@ -1,10 +1,9 @@
 # -*- coding: utf-8 -*-
 import numpy as np
-import tempfile
 from nipype.algorithms.misc import calc_moments
 
 
-def test_skew():
+def test_skew(tmpdir):
     data = """14.62418305  5.916396751  -1.658088086  4.71113546  1.598428608  5.612553811  -5.004056368  -4.057513911
 11.16365251  17.32688599  -3.099920667  2.630189741  2.389709914  0.379332731  -0.2899694205  -4.363591482
 2.059205599  23.90705054  0.7180462297  -1.976963652  7.487682025  -5.583986129  1.094800525  -2.319858134
@@ -126,13 +125,12 @@ def test_skew():
 -0.5057854071  -2.415896554  -9.663571931  -5.714041661  -6.037933426  8.673756933  10.03557773  8.629816199
 3.622185659  0.4716627142  -10.92515308  -3.705286841  -2.776089545  2.271920902  9.251504922  5.744980887
 """
-    with tempfile.NamedTemporaryFile(mode='w', delete=True) as f:
-        f.write(data)
-        f.flush()
-        skewness = calc_moments(f.name, 3)
-        assert np.allclose(skewness, np.array(
-                [-0.23418937314622, 0.2946365564954823, -0.05781002053540932,
-                  -0.3512508282578762, -
-                  0.07035664150233077, -
-                  0.01935867699166935,
-                  0.00483863369427428, 0.21879460029850167]))
+    f = tmpdir.join("filetest")
+    f.write(data)
+    skewness = calc_moments(f.strpath, 3)
+    assert np.allclose(skewness, np.array(
+            [-0.23418937314622, 0.2946365564954823, -0.05781002053540932,
+              -0.3512508282578762, -
+              0.07035664150233077, -
+              0.01935867699166935,
+              0.00483863369427428, 0.21879460029850167]))

nipype/algorithms/tests/test_normalize_tpms.py

@@ -18,7 +18,6 @@
 
 
 def test_normalize_tpms(tmpdir):
-    tempdir = str(tmpdir)
 
     in_mask = example_data('tpms_msk.nii.gz')
     mskdata = nb.load(in_mask, mmap=NUMPY_MMAP).get_data()
@@ -30,8 +29,8 @@ def test_normalize_tpms(tmpdir):
 
     for i in range(3):
         mapname = example_data('tpm_%02d.nii.gz' % i)
-        filename = os.path.join(tempdir, 'modtpm_%02d.nii.gz' % i)
-        out_files.append(os.path.join(tempdir, 'normtpm_%02d.nii.gz' % i))
+        filename = tmpdir.join('modtpm_%02d.nii.gz' % i).strpath
+        out_files.append(tmpdir.join('normtpm_%02d.nii.gz' % i).strpath)
 
         im = nb.load(mapname, mmap=NUMPY_MMAP)
         data = im.get_data()

nipype/algorithms/tests/test_overlap.py

@@ -20,7 +20,7 @@ def check_close(val1, val2):
     in1 = example_data('segmentation0.nii.gz')
     in2 = example_data('segmentation1.nii.gz')
 
-    os.chdir(str(tmpdir))
+    tmpdir.chdir()
     overlap = Overlap()
     overlap.inputs.volume1 = in1
     overlap.inputs.volume2 = in1

nipype/algorithms/tests/test_splitmerge.py

@@ -14,13 +14,13 @@ def test_split_and_merge(tmpdir):
     from nipype.algorithms.misc import split_rois, merge_rois
 
     in_mask = example_data('tpms_msk.nii.gz')
-    dwfile = op.join(str(tmpdir), 'dwi.nii.gz')
+    dwfile = tmpdir.join('dwi.nii.gz').strpath
     mskdata = nb.load(in_mask, mmap=NUMPY_MMAP).get_data()
     aff = nb.load(in_mask, mmap=NUMPY_MMAP).affine
 
     dwshape = (mskdata.shape[0], mskdata.shape[1], mskdata.shape[2], 6)
     dwdata = np.random.normal(size=dwshape)
-    os.chdir(str(tmpdir))
+    tmpdir.chdir()
     nb.Nifti1Image(dwdata.astype(np.float32),
                    aff, None).to_filename(dwfile)
 

nipype/algorithms/tests/test_tsnr.py

@@ -30,8 +30,7 @@ class TestTSNR():
     @pytest.fixture(autouse=True)
     def setup_class(self, tmpdir):
         # setup temp folder
-        self.temp_dir = str(tmpdir)
-        os.chdir(self.temp_dir)
+        tmpdir.chdir()
 
         utils.save_toy_nii(self.fake_data, self.in_filenames['in_file'])
 

nipype/caching/tests/test_memory.py

@@ -25,7 +25,7 @@ def test_caching(tmpdir):
     try:
         # Prevent rerun to check that evaluation is computed only once
         config.set('execution', 'stop_on_first_rerun', 'true')
-        mem = Memory(str(tmpdir))
+        mem = Memory(tmpdir.strpath)
         first_nb_run = nb_runs
         results = mem.cache(SideEffectInterface)(input1=2, input2=1)
         assert nb_runs == first_nb_run + 1

nipype/interfaces/afni/model.py

@@ -260,7 +260,7 @@ class Deconvolve(AFNICommand):
     >>> deconvolve.inputs.stim_label = [(1, 'Houses')]
     >>> deconvolve.inputs.gltsym = ['SYM: +Houses']
     >>> deconvolve.inputs.glt_label = [(1, 'Houses')]
-    >>> deconvolve.cmdline  # doctest: +ALLOW_UNICODE
+    >>> deconvolve.cmdline
     "3dDeconvolve -input functional.nii functional2.nii -bucket output.nii -x1D output.1D -num_stimts 1 -stim_times 1 timeseries.txt 'SPMG1(4)' -stim_label 1 Houses -num_glt 1 -gltsym 'SYM: +Houses' -glt_label 1 Houses"
     >>> res = deconvolve.run()  # doctest: +SKIP
     """
@@ -574,7 +574,7 @@ class Remlfit(AFNICommand):
     >>> remlfit.inputs.out_file = 'output.nii'
     >>> remlfit.inputs.matrix = 'output.1D'
     >>> remlfit.inputs.gltsym = [('SYM: +Lab1 -Lab2', 'TestSYM'), ('timeseries.txt', 'TestFile')]
-    >>> remlfit.cmdline  # doctest: +ALLOW_UNICODE
+    >>> remlfit.cmdline
     '3dREMLfit -gltsym "SYM: +Lab1 -Lab2" TestSYM -gltsym "timeseries.txt" TestFile -input "functional.nii functional2.nii" -matrix output.1D -Rbuck output.nii'
     >>> res = remlfit.run()  # doctest: +SKIP
     """
@@ -660,7 +660,7 @@ class Synthesize(AFNICommand):
     >>> synthesize.inputs.cbucket = 'functional.nii'
     >>> synthesize.inputs.matrix = 'output.1D'
     >>> synthesize.inputs.select = ['baseline']
-    >>> synthesize.cmdline  # doctest: +ALLOW_UNICODE
+    >>> synthesize.cmdline
     '3dSynthesize -cbucket functional.nii -matrix output.1D -select baseline'
     >>> syn = synthesize.run()  # doctest: +SKIP
     """