pre-refactor tests

Author: Shoshana Berleant

nipype/algorithms/tests/test_tsnr.py

@@ -4,6 +4,7 @@
 from nipype.testing import assert_equal, assert_true, skipif, utils
 from nipype.algorithms.misc import TSNR
 
+from hashlib import sha1
 import unittest
 import mock
 import nibabel as nb
@@ -17,9 +18,11 @@ class TestTSNR(unittest.TestCase):
     regress_poly = traits.Range(low=1, desc='Remove polynomials')
     '''
 
-    filenames = {
+    in_filenames = {
         'in_file': 'tsnrinfile.nii',
+    }
 
+    out_filenames = {
         # default output file names
         'detrended_file': '/home/ubuntu/nipype/detrend.nii.gz',
         'mean_file':  '/home/ubuntu/nipype/mean.nii.gz',
@@ -29,17 +32,80 @@ class TestTSNR(unittest.TestCase):
 
     def setUp(self):
         # setup
-        utils.save_toy_nii(self.fake_data, self.filenames['in_file'])
+        utils.save_toy_nii(self.fake_data, self.in_filenames['in_file'])
 
     def test_tsnr(self):
-        # setup
         # run
-        tsnrresult = TSNR(in_file=self.in_file_name, regress_poly=1)
+        tsnrresult = TSNR(in_file=self.in_filenames['in_file']).run()
+
+        # assert
+        self.assert_expected_outputs(tsnrresult, {
+            'mean_file':   '1a55bcdf49901f25a2a838c90769989b9e4f2f19',
+            'stddev_file': '0ba52a51fae90a9db6090c735432df5b742d663a',
+            'tsnr_file': 'a794fc55766c8ad725437d3ff8b1153bd5f6e9b0'
+        })
+
+    def test_tsnr_withpoly1(self):
+        # run
+        tsnrresult = TSNR(in_file=self.in_filenames['in_file'],
+                          regress_poly=1).run()
+
         # assert
-        # cleanup
+        self.assert_expected_outputs_poly(tsnrresult, {
+            'detrended_file': 'ee4f6c0b0e8c547617fc11aa50cf659436f9ccf0',
+            'mean_file': '1a55bcdf49901f25a2a838c90769989b9e4f2f19',
+            'stddev_file': 'e61d94e3cfea20b0c86c81bfdf80d82c55e9203b',
+            'tsnr_file': 'a49f1cbd88f2aa71183dcd7aa4b86b17df622f0c'
+        })
+
+    def test_tsnr_withpoly2(self):
+        # run
+        tsnrresult = TSNR(in_file=self.in_filenames['in_file'],
+                          regress_poly=2).run()
+
+        # assert
+        self.assert_expected_outputs_poly(tsnrresult, {
+            'detrended_file': '22cb7f058d61cc090eb1a9dd7d31550bd4362a61',
+            'mean_file': '4cee6776461f6bc238d11a55c0a8d1947a5732df',
+            'stddev_file': '7267de4d9b63fcc553115c0198f1fa3bbb6a5a13',
+            'tsnr_file': '1c6ed05f94806838f7b563df65900f37e60f8a6d'
+        })
+
+    def test_tsnr_withpoly3(self):
+        # run
+        tsnrresult = TSNR(in_file=self.in_filenames['in_file'],
+                          regress_poly=3).run()
+
+        # assert
+        self.assert_expected_outputs_poly(tsnrresult, {
+            'detrended_file': '3f2c1c7da233f128a7020b6fed79d6be2ec59fca',
+            'mean_file': '4cee6776461f6bc238d11a55c0a8d1947a5732df',
+            'stddev_file': '82bb793b76fab503d1d6b3e2d1b20a1bdebd7a2a',
+            'tsnr_file': 'e004bd6096a0077b15058aabd4b0339bf6e21f64'
+        })
+
+    def assert_expected_outputs_poly(self, tsnrresult, hash_dict):
+        assert_equal(tsnrresult.outputs.detrended_file,
+                     self.out_filenames['detrended_file'])
+        self.assert_expected_outputs(tsnrresult, hash_dict)
+
+    def assert_expected_outputs(self, tsnrresult, hash_dict):
+        self.assert_default_outputs(tsnrresult.outputs)
+        self.assert_unchanged(hash_dict)
+
+    def assert_default_outputs(self, outputs):
+        assert_equal(outputs.mean_file, self.out_filenames['mean_file'])
+        assert_equal(outputs.stddev_file, self.out_filenames['stddev_file'])
+        assert_equal(outputs.tsnr_file, self.out_filenames['tsnr_file'])
+
+    def assert_unchanged(self, expected_hashes):
+        for key, hexhash in expected_hashes.iteritems():
+            data = np.asanyarray(nb.load(self.out_filenames[key])._data)
+            assert_equal(sha1(str(data)).hexdigest(), hexhash)
 
     def tearDown(self):
-        utils.remove_nii(self.filenames.values())
+        utils.remove_nii(self.in_filenames.values())
+        utils.remove_nii(self.out_filenames.values())
 
     fake_data = np.array([[[[2, 4, 3, 9, 1],
                             [3, 6, 4, 7, 4]],