def test_Method(self):
"""Test the normal functioning of the method."""
# test training with good and bad images
irs = IntensityRangeStandardization()
irs.train(TestIntensityRangeStandardization.good_trainingset + [TestIntensityRangeStandardization.bad_image])
irs.transform(TestIntensityRangeStandardization.bad_image)
# test equal methods
irs = IntensityRangeStandardization()
irs_ = irs.train(TestIntensityRangeStandardization.good_trainingset)
self.assertEqual(irs, irs_)
irs = IntensityRangeStandardization()
irs.train(TestIntensityRangeStandardization.good_trainingset)
timages = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages.append(irs.transform(i))
irs = IntensityRangeStandardization()
irs_, timages_ = irs.train_transform(TestIntensityRangeStandardization.good_trainingset)
self.assertEqual(irs, irs_, 'instance returned by transform() method is not the same as the once initialized')
for ti, ti_ in zip(timages, timages_):
numpy.testing.assert_allclose(ti, ti_, err_msg = 'train_transform() failed to produce the same results as transform()')
# test pickling
irs = IntensityRangeStandardization()
irs_ = irs.train(TestIntensityRangeStandardization.good_trainingset)
timages = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages.append(irs.transform(i))
with tempfile.TemporaryFile() as f:
pickle.dump(irs, f)
f.seek(0, 0)
irs_ = pickle.load(f)
timages_ = []
for i in TestIntensityRangeStandardization.good_trainingset:
timages_.append(irs_.transform(i))
for ti, ti_ in zip(timages, timages_):
numpy.testing.assert_allclose(ti, ti_, err_msg = 'pickling failed to preserve the instances model')
