pytest cleaning (#2252)

* changing str(tmpdir) to tmpdir.strpath in tests

* using tmpdir.join where its possible (nibabel doesnt like py.path.local)

* using tmpdir.join().strpath in tests with nibabel or other funcions that dont take path.local

* moving doctest ALLOW_UNICODE to pytest.ini, so doesnt have to be included in the docstrings

* fixin one test

* Revert "moving doctest ALLOW_UNICODE to pytest.ini, so doesnt have to be included in the docstrings"

This reverts commit 952fdc9.
My script removed to many things from doctest, will update it.

* moving doctest ALLOW_UNICODE and NORMALIZE_WHITESPACE to pytest.ini, so doesnt have to be included in the docstrings

* moving to temporary directories, so tests can be run in readonly; adding conftest.py with datadir variable and some basic libraries (so you dont have to import them in doctests)

* removing almost all reminders of tempfile package, one test doesn work(TODO)

* more cleaning: mostly moving to tmpdir.chdir and tmpdir.check

* small fix

* small fix

* changing 4 more tests to avoid errors on read-only

* changing crashfile dir in one test to avoid read-only problems

* fixing the DataSink test so it doesnt go to recursive loop (problem related to tmpdir that is the same in fixtures and test functions, DataSInk was trying copy dir to subdir etc.)

* adding testsetup directives to remove some parts from documentation

Author: djarecka

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
     """