class Test_DRGS_demo(VMATMixin, unittest.TestCase):
"""Tests of the result values of the DRGS demo images."""
def setUp(self):
self.vmat = VMAT()
self.vmat.load_demo_image('drgs')
self.vmat.settings.x_offset = 20
def test_demo(self):
"""Run the demo; no errors should arise."""
self.vmat.run_demo_drgs()
def test_overall_passed(self):
"""Test that the overall pass flag is true for default settings"""
super().test_overall_passed('drgs')
def test_segment_positions(self):
"""Test various values of the Segments."""
self.vmat.analyze('drgs')
segment_dict = {0: Point(161, 192), 4: Point(314, 192)}
super().test_segment_positions(segment_dict)
def test_segment_values(self):
self.vmat.analyze('drgs')
segment_dict = {
0: {'r_dev': 0.965, 'r_corr': 101.85},
4: {'r_dev': -0.459, 'r_corr': 100.42},
}
super().test_segment_values(segment_dict)
class Test_MLCS_demo(VMATMixin, unittest.TestCase):
"""Tests of the result values of the DRMLC demo images."""
def setUp(self):
self.vmat = VMAT()
self.vmat.load_demo_image('mlcs')
def test_demo(self):
self.vmat.run_demo_mlcs()
def test_overall_passed(self):
"""Test that the overall pass flag is true for default settings"""
super().test_overall_passed('mlcs')
def test_segment_positions(self):
"""Test various values of the Segments."""
self.vmat.analyze('mlcs')
segment_dict = {0: Point(170, 192), 2: Point(285, 192)}
super().test_segment_positions(segment_dict)
def test_segment_values(self):
self.vmat.analyze('mlcs')
segment_dict = {
0: {'r_dev': 0.437, 'r_corr': 100.89},
2: {'r_dev': -0.405, 'r_corr': 100.04},
}
super().test_segment_values(segment_dict)
class TestGeneral(unittest.TestCase):
"""Generic tests for VMAT class."""
def setUp(self):
self.vmat = VMAT()
def test_demo_image_loads(self):
"""Test the that demo images load properly."""
# shouldn't raise
self.vmat.load_demo_image('drgs')
self.vmat.load_demo_image('mlcs')
def test_analyze_without_both_images_loaded(self):
"""Raise an error if both images aren't loaded when analyzing."""
self.assertRaises(AttributeError, self.vmat.analyze, 'mlcs')
self.vmat.load_demo_image('drgs')
self.vmat.analyze('drgs') # shouldn't raise
def test_img_loaded_tags(self):
"""Test the 'is_loaded' type tags."""
self.assertFalse(self.vmat.open_img_is_loaded)
self.assertFalse(self.vmat.dmlc_img_is_loaded)
self.vmat.load_demo_image()
self.assertTrue(self.vmat.open_img_is_loaded)
self.assertTrue(self.vmat.dmlc_img_is_loaded)
def test_number_of_segments(self):
"""Test that the right amount of segments are constructed based on the given test."""
self.vmat.load_demo_image()
self.vmat.analyze('drgs')
self.assertEqual(len(self.vmat.segments), 7)
self.vmat.analyze('mlcs')
self.assertEqual(len(self.vmat.segments), 4)
def test_passing_3_images(self):
"""Test passing the wrong number of images."""
with self.assertRaises(ValueError):
self.vmat.load_images(('', '', ''))
def test_from_urls(self):
urls = ['https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_dmlc.dcm',
'https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_open.dcm']
vmat = VMAT.from_urls(urls)
def test_img_inversion(self):
"""Check that the demo images indeed get inverted."""
self.vmat.load_demo_image()
top_corner_before = self.vmat.image_open.array[:20, :20].mean()
self.vmat._check_img_inversion()
top_corner_after = self.vmat.image_open.array[:20, :20].mean()
self.assertNotEqual(top_corner_before, top_corner_after)
class Test_general(unittest.TestCase):
"""Generic tests for VMAT class."""
def setUp(self):
self.vmat = VMAT()
def test_demo_image_loads(self):
"""Test the that demo images load properly."""
# shouldn't raise
self.vmat.load_demo_image('drgs')
self.vmat.load_demo_image('mlcs')
def test_analyze_without_both_images_loaded(self):
"""Raise an error if both images aren't loaded when analyzing."""
self.assertRaises(AttributeError, self.vmat.analyze, 'mlcs')
self.vmat.load_demo_image('drgs')
self.vmat.analyze('drgs') # shouldn't raise
def test_img_loaded_tags(self):
"""Test the 'is_loaded' type tags."""
self.assertFalse(self.vmat.open_img_is_loaded)
self.assertFalse(self.vmat.dmlc_img_is_loaded)
self.vmat.load_demo_image()
self.assertTrue(self.vmat.open_img_is_loaded)
self.assertTrue(self.vmat.dmlc_img_is_loaded)
def test_number_of_segments(self):
"""Test that the right amount of segments are constructed based on the given test."""
self.vmat.load_demo_image()
self.vmat.analyze('drgs')
self.assertEqual(len(self.vmat.segments), 7)
self.vmat.analyze('mlcs')
self.assertEqual(len(self.vmat.segments), 4)
class TestLoading(TestCase):
"""Tests of the various loading schemas."""
def setUp(self):
self.vmat = VMAT()
def test_load_image(self):
good_name = osp.join(vmat_test_files_dir, 'no_test_type_dmlc.dcm')
bad_name = osp.join(vmat_test_files_dir, 'no_test_or_image_type_1.dcm')
# image type can be determined from a good name
self.vmat.load_image(good_name)
# but not a bad one
with self.assertRaises(ValueError):
self.vmat.load_image(bad_name)
def test_from_urls(self):
urls = [
'https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_dmlc.dcm',
'https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_open.dcm'
]
VMAT.from_urls(urls) # shouldn't raise
def test_passing_3_images(self):
"""Test passing the wrong number of images."""
with self.assertRaises(ValueError):
self.vmat.load_images(('', '', ''))
def test_demo_image_loads(self):
"""Test the that demo images load properly."""
# shouldn't raise
self.vmat.load_demo_image(DRGS)
self.vmat.load_demo_image(DRMLC)
def test_from_zip(self):
path = osp.join(vmat_test_files_dir, 'DRMLC.zip')
v = VMAT.from_zip(path)
v.analyze()
def test_loading_with_bad_names(self):
one = osp.join(vmat_test_files_dir, 'no_test_or_image_type_1.dcm')
two = osp.join(vmat_test_files_dir, 'no_test_or_image_type_2.dcm')
with self.assertRaises(ValueError):
self.vmat.load_images((one, two))
# but will work when everything is specified
self.vmat.load_image(one, OPEN)
self.vmat.load_image(two, DMLC)
self.vmat.analyze(DRMLC)
class TestLoading(TestCase):
"""Tests of the various loading schemas."""
def setUp(self):
self.vmat = VMAT()
def test_load_image(self):
good_name = osp.join(vmat_test_files_dir, 'no_test_type_dmlc.dcm')
bad_name = osp.join(vmat_test_files_dir, 'no_test_or_image_type_1.dcm')
# image type can be determined from a good name
self.vmat.load_image(good_name)
# but not a bad one
with self.assertRaises(ValueError):
self.vmat.load_image(bad_name)
def test_from_urls(self):
urls = ['https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_dmlc.dcm',
'https://s3.amazonaws.com/assuranceqa-staging/uploads/imgs/DRGS_open.dcm']
VMAT.from_urls(urls) # shouldn't raise
def test_passing_3_images(self):
"""Test passing the wrong number of images."""
with self.assertRaises(ValueError):
self.vmat.load_images(('', '', ''))
def test_demo_image_loads(self):
"""Test the that demo images load properly."""
# shouldn't raise
self.vmat.load_demo_image(DRGS)
self.vmat.load_demo_image(DRMLC)
def test_from_zip(self):
path = osp.join(vmat_test_files_dir, 'DRMLC.zip')
v = VMAT.from_zip(path)
v.analyze()
def test_loading_with_bad_names(self):
one = osp.join(vmat_test_files_dir, 'no_test_or_image_type_1.dcm')
two = osp.join(vmat_test_files_dir, 'no_test_or_image_type_2.dcm')
with self.assertRaises(ValueError):
self.vmat.load_images((one, two))
# but will work when everything is specified
self.vmat.load_image(one, OPEN)
self.vmat.load_image(two, DMLC)
self.vmat.analyze(DRMLC)
class TestGeneral(TestCase):
"""Generic tests for VMAT class."""
def setUp(self):
self.vmat = VMAT()
def test_analyze_without_both_images_loaded(self):
"""Raise an error if both images aren't loaded when analyzing."""
self.assertRaises(AttributeError, self.vmat.analyze, DRMLC)
self.vmat.load_demo_image('drgs')
self.vmat.analyze('drgs') # shouldn't raise
def test_img_loaded_tags(self):
"""Test the 'is_loaded' type tags."""
self.assertFalse(self.vmat.open_img_is_loaded)
self.assertFalse(self.vmat.dmlc_img_is_loaded)
self.vmat.load_demo_image()
self.assertTrue(self.vmat.open_img_is_loaded)
self.assertTrue(self.vmat.dmlc_img_is_loaded)
def test_img_inversion(self):
"""Check that the demo images indeed get inverted."""
self.vmat.load_demo_image()
top_corner_before = self.vmat.image_open.array[:20, :20].mean()
self.vmat._check_img_inversion()
top_corner_after = self.vmat.image_open.array[:20, :20].mean()
self.assertNotEqual(top_corner_before, top_corner_after)
def test_analyze_without_test_type(self):
dmlc = osp.join(vmat_test_files_dir, 'no_test_type_dmlc.dcm')
opn = osp.join(vmat_test_files_dir, 'no_test_type_open.dcm')
self.vmat.load_images((dmlc, opn))
with self.assertRaises(ValueError):
self.vmat.analyze()
# but will run when test type is passed
self.vmat.analyze('drmlc')
def test_failure_with_tight_tolerance(self):
self.vmat.load_demo_image()
self.vmat.analyze(tolerance=0.1)
self.vmat.return_results()
class TestGeneral(TestCase):
"""Generic tests for VMAT class."""
def setUp(self):
self.vmat = VMAT()
def test_analyze_without_both_images_loaded(self):
"""Raise an error if both images aren't loaded when analyzing."""
self.assertRaises(AttributeError, self.vmat.analyze, DRMLC)
self.vmat.load_demo_image('drgs')
self.vmat.analyze('drgs') # shouldn't raise
def test_img_loaded_tags(self):
"""Test the 'is_loaded' type tags."""
self.assertFalse(self.vmat.open_img_is_loaded)
self.assertFalse(self.vmat.dmlc_img_is_loaded)
self.vmat.load_demo_image()
self.assertTrue(self.vmat.open_img_is_loaded)
self.assertTrue(self.vmat.dmlc_img_is_loaded)
def test_img_inversion(self):
"""Check that the demo images indeed get inverted."""
self.vmat.load_demo_image()
top_corner_before = self.vmat.image_open.array[:20, :20].mean()
self.vmat._check_img_inversion()
top_corner_after = self.vmat.image_open.array[:20, :20].mean()
self.assertNotEqual(top_corner_before, top_corner_after)
def test_analyze_without_test_type(self):
dmlc = osp.join(vmat_test_files_dir, 'no_test_type_dmlc.dcm')
opn = osp.join(vmat_test_files_dir, 'no_test_type_open.dcm')
self.vmat.load_images((dmlc, opn))
with self.assertRaises(ValueError):
self.vmat.analyze()
# but will run when test type is passed
self.vmat.analyze('drmlc')
def test_failure_with_tight_tolerance(self):
self.vmat.load_demo_image()
self.vmat.analyze(tolerance=0.1)
self.vmat.return_results()
