explicitly importing numpy.testing where assert_equal or assert_almost_equal from numpy is used; small cleaning of imports that are not used

Author: djarecka

nipype/algorithms/tests/test_mesh_ops.py

@@ -6,7 +6,8 @@
 import os
 
 import pytest
-from nipype.testing import assert_almost_equal, example_data
+import nipype.testing as npt
+from nipype.testing import example_data
 import numpy as np
 from nipype.algorithms import mesh as m
 from ...interfaces import vtkbase as VTKInfo
@@ -56,10 +57,10 @@ def test_trans_distances(tmpdir):
     dist.inputs.surface2 = warped_surf
     dist.inputs.out_file = os.path.join(tempdir, 'distance.npy')
     res = dist.run()
-    assert_almost_equal(res.outputs.distance, np.linalg.norm(inc), 4)
+    npt.assert_almost_equal(res.outputs.distance, np.linalg.norm(inc), 4)
     dist.inputs.weighting = 'area'
     res = dist.run()
-    assert_almost_equal(res.outputs.distance, np.linalg.norm(inc), 4)
+    npt.assert_almost_equal(res.outputs.distance, np.linalg.norm(inc), 4)
 
 
 @pytest.mark.skipif(VTKInfo.no_tvtk(), reason="tvtk is not installed")

nipype/algorithms/tests/test_modelgen.py

@@ -10,7 +10,7 @@
 import numpy as np
 
 import pytest
-from nipype.testing import assert_almost_equal
+import numpy.testing as npt
 from nipype.interfaces.base import Bunch, TraitError
 from nipype.algorithms.modelgen import (SpecifyModel, SpecifySparseModel,
                                         SpecifySPMModel)
@@ -38,21 +38,21 @@ def test_modelgen1(tmpdir):
     assert len(res.outputs.session_info) == 2
     assert len(res.outputs.session_info[0]['regress']) == 0
     assert len(res.outputs.session_info[0]['cond']) == 1
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([12, 300, 600, 1080]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([12, 300, 600, 1080]))
     info = [Bunch(conditions=['cond1'], onsets=[[2]], durations=[[1]]),
             Bunch(conditions=['cond1'], onsets=[[3]], durations=[[1]])]
     s.inputs.subject_info = deepcopy(info)
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6.]))
-    assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][0]['duration']), np.array([6.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][0]['duration']), np.array([6.]))
     info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
             Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]])]
     s.inputs.subject_info = deepcopy(info)
     s.inputs.input_units = 'scans'
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6., 6.]))
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([6., ]))
-    assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([6., 6.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6., 6.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([6., ]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([6., 6.]))
 
 
 def test_modelgen_spm_concat(tmpdir):
@@ -79,22 +79,22 @@ def test_modelgen_spm_concat(tmpdir):
     assert len(res.outputs.session_info[0]['regress']) == 1
     assert np.sum(res.outputs.session_info[0]['regress'][0]['val']) == 30
     assert len(res.outputs.session_info[0]['cond']) == 1
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]))
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1., 1., 1.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1., 1., 1.]))
 
     # Test case of scans as output units instead of seconds
     setattr(s.inputs, 'output_units', 'scans')
     assert s.inputs.output_units == 'scans'
     s.inputs.subject_info = deepcopy(info)
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6)
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0]) / 6)
 
     # Test case for no concatenation with seconds as output units
     s.inputs.concatenate_runs = False
     s.inputs.subject_info = deepcopy(info)
     s.inputs.output_units = 'secs'
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0]))
+    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')
@@ -105,10 +105,10 @@ def test_modelgen_spm_concat(tmpdir):
             Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
     s.inputs.subject_info = deepcopy(info)
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1.]))
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., ]))
-    assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([1., 1.]))
-    assert_almost_equal(np.array(res.outputs.session_info[2]['cond'][1]['duration']), np.array([1., ]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., ]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([1., 1.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[2]['cond'][1]['duration']), np.array([1., ]))
 
     # Test case for variable number of events in concatenated runs, sometimes unique.
     s.inputs.concatenate_runs = True
@@ -117,8 +117,8 @@ def test_modelgen_spm_concat(tmpdir):
             Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
     s.inputs.subject_info = deepcopy(info)
     res = s.run()
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1.]))
-    assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., 1., 1., 1.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1.]))
+    npt.assert_almost_equal(np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., 1., 1., 1.]))
 
 
 def test_modelgen_sparse(tmpdir):
@@ -148,12 +148,12 @@ def test_modelgen_sparse(tmpdir):
 
     s.inputs.model_hrf = True
     res = s.run()
-    assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384)
+    npt.assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384)
     assert len(res.outputs.session_info[0]['regress']) == 1
     s.inputs.use_temporal_deriv = True
     res = s.run()
 
     assert len(res.outputs.session_info[0]['regress']) == 2
-    assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384)
-    assert_almost_equal(res.outputs.session_info[1]['regress'][1]['val'][5], 0.007671459162258378)
+    npt.assert_almost_equal(res.outputs.session_info[0]['regress'][0]['val'][0], 0.016675298129743384)
+    npt.assert_almost_equal(res.outputs.session_info[1]['regress'][1]['val'][5], 0.007671459162258378)
 

nipype/algorithms/tests/test_rapidart.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 
-from ...testing import assert_equal, assert_almost_equal
+import numpy.testing as npt
 from .. import rapidart as ra
 from ...interfaces.base import Bunch
 
@@ -33,38 +33,38 @@ def test_ad_output_filenames():
 
 def test_ad_get_affine_matrix():
     matrix = ra._get_affine_matrix(np.array([0]), 'SPM')
-    assert_equal(matrix, np.eye(4))
+    npt.assert_equal(matrix, np.eye(4))
     # test translation
     params = [1, 2, 3]
     matrix = ra._get_affine_matrix(params, 'SPM')
     out = np.eye(4)
     out[0:3, 3] = params
-    assert_equal(matrix, out)
+    npt.assert_equal(matrix, out)
     # test rotation
     params = np.array([0, 0, 0, np.pi / 2, np.pi / 2, np.pi / 2])
     matrix = ra._get_affine_matrix(params, 'SPM')
     out = np.array([0, 0, 1, 0, 0, -1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]).reshape((4, 4))
-    assert_almost_equal(matrix, out)
+    npt.assert_almost_equal(matrix, out)
     # test scaling
     params = np.array([0, 0, 0, 0, 0, 0, 1, 2, 3])
     matrix = ra._get_affine_matrix(params, 'SPM')
     out = np.array([1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1]).reshape((4, 4))
-    assert_equal(matrix, out)
+    npt.assert_equal(matrix, out)
     # test shear
     params = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 3])
     matrix = ra._get_affine_matrix(params, 'SPM')
     out = np.array([1, 1, 2, 0, 0, 1, 3, 0, 0, 0, 1, 0, 0, 0, 0, 1]).reshape((4, 4))
-    assert_equal(matrix, out)
+    npt.assert_equal(matrix, out)
 
 
 def test_ad_get_norm():
     params = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, np.pi / 4, np.pi / 4,
                        np.pi / 4, 0, 0, 0, -np.pi / 4,
                        -np.pi / 4, -np.pi / 4]).reshape((3, 6))
     norm, _ = ra._calc_norm(params, False, 'SPM')
-    assert_almost_equal(norm, np.array([18.86436316, 37.74610158, 31.29780829]))
+    npt.assert_almost_equal(norm, np.array([18.86436316, 37.74610158, 31.29780829]))
     norm, _ = ra._calc_norm(params, True, 'SPM')
-    assert_almost_equal(norm, np.array([0., 143.72192614, 173.92527131]))
+    npt.assert_almost_equal(norm, np.array([0., 143.72192614, 173.92527131]))
 
 
 def test_sc_init():

nipype/algorithms/tests/test_tsnr.py

@@ -1,11 +1,12 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 
-from ...testing import (assert_equal, assert_almost_equal, utils)
+from ...testing import utils
 from ..confounds import TSNR
 from .. import misc
 
 import pytest
+import numpy.testing as npt
 import mock
 import nibabel as nb
 import numpy as np
@@ -94,27 +95,27 @@ def test_warning(self, mock_warn):
         assert True in [args[0].count('confounds') > 0 for _, args, _ in mock_warn.mock_calls]
 
     def assert_expected_outputs_poly(self, tsnrresult, expected_ranges):
-        assert_equal(os.path.basename(tsnrresult.outputs.detrended_file),
-                     self.out_filenames['detrended_file'])
+        assert os.path.basename(tsnrresult.outputs.detrended_file) == \
+            self.out_filenames['detrended_file']
         self.assert_expected_outputs(tsnrresult, expected_ranges)
 
     def assert_expected_outputs(self, tsnrresult, expected_ranges):
         self.assert_default_outputs(tsnrresult.outputs)
         self.assert_unchanged(expected_ranges)
 
     def assert_default_outputs(self, outputs):
-        assert_equal(os.path.basename(outputs.mean_file),
-                     self.out_filenames['mean_file'])
-        assert_equal(os.path.basename(outputs.stddev_file),
-                     self.out_filenames['stddev_file'])
-        assert_equal(os.path.basename(outputs.tsnr_file),
-                     self.out_filenames['tsnr_file'])
+        assert os.path.basename(outputs.mean_file) == \
+            self.out_filenames['mean_file']
+        assert os.path.basename(outputs.stddev_file) == \
+            self.out_filenames['stddev_file']
+        assert os.path.basename(outputs.tsnr_file) == \
+            self.out_filenames['tsnr_file']
 
     def assert_unchanged(self, expected_ranges):
         for key, (min_, max_) in expected_ranges.items():
             data = np.asarray(nb.load(self.out_filenames[key])._data)
-            assert_almost_equal(np.amin(data), min_, decimal=1)
-            assert_almost_equal(np.amax(data), max_, decimal=1)
+            npt.assert_almost_equal(np.amin(data), min_, decimal=1)
+            npt.assert_almost_equal(np.amax(data), max_, decimal=1)
 
 
     fake_data = np.array([[[[2, 4, 3, 9, 1],

nipype/interfaces/nitime/tests/test_nitime.py

@@ -6,8 +6,7 @@
 
 import numpy as np
 
-import pytest, pdb
-from nipype.testing import (assert_equal, assert_raises, skipif)
+import pytest
 from nipype.testing import example_data
 import nipype.interfaces.nitime as nitime
 

nipype/interfaces/spm/tests/test_preprocess.py

@@ -8,8 +8,6 @@
 import numpy as np
 
 import pytest
-from nipype.testing import (assert_equal, assert_false, assert_true,
-                            assert_raises, skipif)
 import nibabel as nb
 import nipype.interfaces.spm as spm
 from nipype.interfaces.spm import no_spm

nipype/interfaces/tests/test_base.py

@@ -16,7 +16,6 @@
 import nipype.interfaces.base as nib
 from nipype.utils.filemanip import split_filename
 from nipype.interfaces.base import Undefined, config
-from traits.testing.nose_tools import skip
 import traits.api as traits
 
 @pytest.mark.parametrize("args", [
@@ -97,7 +96,6 @@ class spec(nib.TraitedSpec):
     infields = spec(foo=1)
     hashval = ([('foo', 1), ('goo', '0.0000000000')], 'e89433b8c9141aa0fda2f8f4d662c047')
     assert infields.get_hashval() == hashval
-    # yield assert_equal, infields.hashval[1], hashval[1]
     assert infields.__repr__() == '\nfoo = 1\ngoo = 0.0\n'
 
 

nipype/interfaces/tests/test_io.py

@@ -14,7 +14,6 @@
 
 import pytest
 import nipype
-from nipype.testing import assert_equal, assert_true, assert_false, skipif
 import nipype.interfaces.io as nio
 from nipype.interfaces.base import Undefined
 

nipype/interfaces/tests/test_nilearn.py

@@ -7,12 +7,12 @@
 import numpy as np
 
 from ...testing import utils
-from numpy.testing import assert_almost_equal 
 
 from .. import nilearn as iface
 from ...pipeline import engine as pe
 
 import pytest
+import numpy.testing as npt
 
 no_nilearn = True
 try:
@@ -149,7 +149,7 @@ def assert_expected_output(self, labels, wanted):
             for i, time in enumerate(got):
                 assert len(labels) == len(time)
                 for j, segment in enumerate(time):
-                    assert_almost_equal(segment, wanted[i][j], decimal=1)
+                    npt.assert_almost_equal(segment, wanted[i][j], decimal=1)
 
 
     def teardown_class(self):

nipype/pipeline/engine/tests/test_engine.py

@@ -532,7 +532,6 @@ def _submit_job(self, node, updatehash=False):
         logger.info('Exception: %s' % str(e))
         error_raised = True
     assert error_raised
-    # yield assert_true, 'Submit called' in e
     # rerun to ensure we have outputs
     w1.run(plugin='Linear')
     # set local check