def setUp(self):
super(ParrecTests, self).setUp()
self.libs = Mock()
self.dependencies.getLibraries.return_value = self.libs
self.setupNibabel()
from niprov.parrec import ParrecFile
self.constructor = ParrecFile
self.file = ParrecFile(self.path, dependencies=self.dependencies)
def setUp(self):
super(ParrecTests, self).setUp()
self.libs = Mock()
self.dependencies.getLibraries.return_value = self.libs
self.setupNibabel()
from niprov.parrec import ParrecFile
self.constructor = ParrecFile
self.file = ParrecFile(self.path, dependencies=self.dependencies)
class ParrecTests(BaseFileTests):
def setUp(self):
super(ParrecTests, self).setUp()
self.libs = Mock()
self.dependencies.getLibraries.return_value = self.libs
self.setupNibabel()
from niprov.parrec import ParrecFile
self.constructor = ParrecFile
self.file = ParrecFile(self.path, dependencies=self.dependencies)
def test_Gets_basic_info_from_nibabel_and_returns_it(self):
out = self.file.inspect()
self.assertEqual(out['subject'], 'John Doeish')
self.assertEqual(out['protocol'], 'T1 SENSE')
self.assertEqual(out['acquired'], datetime(2014, 8, 5, 11, 27, 34))
def test_Gets_dimensions(self):
out = self.file.inspect()
self.assertEqual(out['dimensions'], [80,80,10])
def test_Gets_advanced_fields(self):
out = self.file.inspect()
self.assertEqual(out['technique'], 'T1TFE')
self.assertEqual(out['repetition-time'], 4.364)
self.assertEqual(out['field-of-view'], [130., 100., 154.375])
self.assertEqual(out['epi-factor'], 1)
self.assertEqual(out['magnetization-transfer-contrast'], False)
self.assertEqual(out['diffusion'], False)
self.assertEqual(out['duration'], 65)
self.assertEqual(out['subject-position'], 'Head First Supine')
self.assertEqual(out['water-fat-shift'], 1.117)
# per-image
self.assertEqual(out['slice-thickness'], 10.0)
self.assertEqual(out['slice-orientation'], 1)
self.assertEqual(out['echo-time'], 2.0800000000000001)
self.assertEqual(out['flip-angle'], 8.0)
self.assertEqual(out['inversion-time'], 0.0)
def test_getProtocolFields(self):
protocol = self.file.getProtocolFields()
self.assertIn('repetition-time', protocol)
self.assertIn('echo-time', protocol)
def test_multiple_TRs(self):
img = self.libs.nibabel.load.return_value
img.header.general_info['repetition_time'] = numpy.array([130, 450])
self.libs.nibabel.load.return_value = img
out = self.file.inspect()
self.assertEqual(out['repetition-time'], [130, 450])
def test_Tells_camera_to_save_snapshot_to_cache(self):
img = self.libs.nibabel.load.return_value
data = sentinel.imagedata
img.get_data.return_value = data
out = self.file.inspect()
self.camera.saveSnapshot.assert_called_with(data, for_=self.file)
def test_Determines_modality(self):
out = self.file.inspect()
self.assertEqual(out['modality'], 'MRI')
def test_Determines_modality_for_diffusion(self):
img = self.libs.nibabel.load.return_value
img.header.general_info['diffusion'] = 1
self.libs.nibabel.load.return_value = img
out = self.file.inspect()
self.assertEqual(out['modality'], 'DWI')
def test_Preserves_modality_if_inherited(self):
pass # Doesn't have to preserve
def setupNibabel(self):
import numpy
img = Mock()
img.header.general_info = {
'acq_nr': 6,
'angulation': numpy.array([-1.979, 0.546, 0.019]),
'diffusion': 0,
'diffusion_echo_time': 0.0,
'dyn_scan': 0,
'epi_factor': 1,
'exam_date': '2014.08.05 / 11:27:34',
'exam_name': 'test',
'flow_compensation': 0,
'fov': numpy.array([ 130. , 100. , 154.375]),
'max_cardiac_phases': 1,
'max_diffusion_values': 1,
'max_dynamics': 1,
'max_echoes': 1,
'max_gradient_orient': 1,
'max_mixes': 1,
'max_slices': 10,
'mtc': 0,
'nr_label_types': 0,
'off_center': numpy.array([-18.805, 22.157, -17.977]),
'patient_name': 'John Doeish',
'patient_position': 'Head First Supine',
'phase_enc_velocity': numpy.array([ 0., 0., 0.]),
'prep_direction': 'Right-Left',
'presaturation': 0,
'protocol_name': 'T1 SENSE',
'recon_nr': 1,
'repetition_time': 4.364,
'scan_duration': 65.0,
'scan_mode': '3D',
'scan_resolution': numpy.array([76, 62]),
'series_type': 'Image MRSERIES',
'spir': 0,
'tech': 'T1TFE',
'water_fat_shift': 1.117}
img.header.image_defs = numpy.array([ (1, 1, 1, 1, 0, 2, 0, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05, 133, 231, [-1.98, 0.55, 0.02], [-18.79, -22.82, -16.42], 10.0, 0.0, 0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0, 0, 0, 7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1),
(2, 1, 1, 1, 0, 2, 1, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05, 294, 512, [-1.98, 0.55, 0.02], [-18.79, -12.82, -16.77], 10.0, 0.0, 0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0, 0, 0, 7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1),
(3, 1, 1, 1, 0, 2, 2, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05, 427, 742, [-1.98, 0.55, 0.02], [-18.8, -2.83, -17.11], 10.0, 0.0, 0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0, 0, 0, 7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1)],
dtype=[('slice number', '<i8'), ('echo number', '<i8'), ('dynamic scan number', '<i8'), ('cardiac phase number', '<i8'), ('image_type_mr', '<i8'), ('scanning sequence', '<i8'), ('index in REC file', '<i8'), ('image pixel size', '<i8'), ('scan percentage', '<i8'), ('recon resolution', '<i8', (2,)), ('rescale intercept', '<f8'), ('rescale slope', '<f8'), ('scale slope', '<f8'), ('window center', '<i8'), ('window width', '<i8'), ('image angulation', '<f8', (3,)), ('image offcentre', '<f8', (3,)), ('slice thickness', '<f8'), ('slice gap', '<f8'), ('image_display_orientation', '<i8'), ('slice orientation', '<i8'), ('fmri_status_indication', '<i8'), ('image_type_ed_es', '<i8'), ('pixel spacing', '<f8', (2,)), ('echo_time', '<f8'), ('dyn_scan_begin_time', '<f8'), ('trigger_time', '<f8'), ('diffusion_b_factor', '<f8'), ('number of averages', '<i8'), ('image_flip_angle', '<f8'), ('cardiac frequency', '<i8'), ('minimum RR-interval', '<i8'), ('maximum RR-interval', '<i8'), ('TURBO factor', '<i8'), ('Inversion delay', '<f8'), ('diffusion b value number', '<i8'), ('gradient orientation number', '<i8'), ('contrast type', 'S30'), ('diffusion anisotropy type', 'S30'), ('diffusion', '<f8', (3,)), ('label type', '<i8')])
img.shape = (80,80,10)
self.libs.nibabel.load.return_value = img
self.libs.hasDependency.return_value = True
class ParrecTests(BaseFileTests):
def setUp(self):
super(ParrecTests, self).setUp()
self.libs = Mock()
self.dependencies.getLibraries.return_value = self.libs
self.setupNibabel()
from niprov.parrec import ParrecFile
self.constructor = ParrecFile
self.file = ParrecFile(self.path, dependencies=self.dependencies)
def test_Gets_basic_info_from_nibabel_and_returns_it(self):
out = self.file.inspect()
self.assertEqual(out['subject'], 'John Doeish')
self.assertEqual(out['protocol'], 'T1 SENSE')
self.assertEqual(out['acquired'], datetime(2014, 8, 5, 11, 27, 34))
def test_Gets_dimensions(self):
out = self.file.inspect()
self.assertEqual(out['dimensions'], [80, 80, 10])
def test_Gets_advanced_fields(self):
out = self.file.inspect()
self.assertEqual(out['technique'], 'T1TFE')
self.assertEqual(out['repetition-time'], 4.364)
self.assertEqual(out['field-of-view'], [130., 100., 154.375])
self.assertEqual(out['epi-factor'], 1)
self.assertEqual(out['magnetization-transfer-contrast'], False)
self.assertEqual(out['diffusion'], False)
self.assertEqual(out['duration'], 65)
self.assertEqual(out['subject-position'], 'Head First Supine')
self.assertEqual(out['water-fat-shift'], 1.117)
# per-image
self.assertEqual(out['slice-thickness'], 10.0)
self.assertEqual(out['slice-orientation'], 1)
self.assertEqual(out['echo-time'], 2.0800000000000001)
self.assertEqual(out['flip-angle'], 8.0)
self.assertEqual(out['inversion-time'], 0.0)
def test_getProtocolFields(self):
protocol = self.file.getProtocolFields()
self.assertIn('repetition-time', protocol)
self.assertIn('echo-time', protocol)
def test_multiple_TRs(self):
img = self.libs.nibabel.load.return_value
img.header.general_info['repetition_time'] = numpy.array([130, 450])
self.libs.nibabel.load.return_value = img
out = self.file.inspect()
self.assertEqual(out['repetition-time'], [130, 450])
def test_Tells_camera_to_save_snapshot_to_cache(self):
img = self.libs.nibabel.load.return_value
data = sentinel.imagedata
img.get_data.return_value = data
out = self.file.inspect()
self.camera.saveSnapshot.assert_called_with(data, for_=self.file)
def test_Determines_modality(self):
out = self.file.inspect()
self.assertEqual(out['modality'], 'MRI')
def test_Determines_modality_for_diffusion(self):
img = self.libs.nibabel.load.return_value
img.header.general_info['diffusion'] = 1
self.libs.nibabel.load.return_value = img
out = self.file.inspect()
self.assertEqual(out['modality'], 'DWI')
def test_Preserves_modality_if_inherited(self):
pass # Doesn't have to preserve
def setupNibabel(self):
import numpy
img = Mock()
img.header.general_info = {
'acq_nr': 6,
'angulation': numpy.array([-1.979, 0.546, 0.019]),
'diffusion': 0,
'diffusion_echo_time': 0.0,
'dyn_scan': 0,
'epi_factor': 1,
'exam_date': '2014.08.05 / 11:27:34',
'exam_name': 'test',
'flow_compensation': 0,
'fov': numpy.array([130., 100., 154.375]),
'max_cardiac_phases': 1,
'max_diffusion_values': 1,
'max_dynamics': 1,
'max_echoes': 1,
'max_gradient_orient': 1,
'max_mixes': 1,
'max_slices': 10,
'mtc': 0,
'nr_label_types': 0,
'off_center': numpy.array([-18.805, 22.157, -17.977]),
'patient_name': 'John Doeish',
'patient_position': 'Head First Supine',
'phase_enc_velocity': numpy.array([0., 0., 0.]),
'prep_direction': 'Right-Left',
'presaturation': 0,
'protocol_name': 'T1 SENSE',
'recon_nr': 1,
'repetition_time': 4.364,
'scan_duration': 65.0,
'scan_mode': '3D',
'scan_resolution': numpy.array([76, 62]),
'series_type': 'Image MRSERIES',
'spir': 0,
'tech': 'T1TFE',
'water_fat_shift': 1.117
}
img.header.image_defs = numpy.array(
[(1, 1, 1, 1, 0, 2, 0, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05,
133, 231, [-1.98, 0.55, 0.02], [-18.79, -22.82, -16.42], 10.0,
0.0, 0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0,
0, 0, 7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1),
(2, 1, 1, 1, 0, 2, 1, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05,
294, 512, [-1.98, 0.55, 0.02], [-18.79, -12.82, -16.77], 10.0,
0.0, 0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0,
0, 0, 7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1),
(3, 1, 1, 1, 0, 2, 2, 16, 81, [80, 80], 0.0, 1.26032, 2.84925e-05,
427, 742, [-1.98, 0.55, 0.02], [-18.8, -2.83, -17.11], 10.0, 0.0,
0, 1, 0, 2, [1.912, 1.912], 2.08, 0.0, 0.0, 0.0, 1, 8.0, 0, 0, 0,
7, 0.0, 1, 1, '7', '0', [0.0, 0.0, 0.0], 1)],
dtype=[('slice number', '<i8'), ('echo number', '<i8'),
('dynamic scan number', '<i8'),
('cardiac phase number', '<i8'), ('image_type_mr', '<i8'),
('scanning sequence', '<i8'), ('index in REC file', '<i8'),
('image pixel size', '<i8'), ('scan percentage', '<i8'),
('recon resolution', '<i8', (2, )),
('rescale intercept', '<f8'), ('rescale slope', '<f8'),
('scale slope', '<f8'), ('window center', '<i8'),
('window width', '<i8'), ('image angulation', '<f8', (3, )),
('image offcentre', '<f8', (3, )),
('slice thickness', '<f8'), ('slice gap', '<f8'),
('image_display_orientation', '<i8'),
('slice orientation', '<i8'),
('fmri_status_indication', '<i8'),
('image_type_ed_es', '<i8'), ('pixel spacing',
'<f8', (2, )),
('echo_time', '<f8'), ('dyn_scan_begin_time', '<f8'),
('trigger_time', '<f8'), ('diffusion_b_factor', '<f8'),
('number of averages', '<i8'), ('image_flip_angle', '<f8'),
('cardiac frequency', '<i8'),
('minimum RR-interval', '<i8'),
('maximum RR-interval', '<i8'), ('TURBO factor', '<i8'),
('Inversion delay', '<f8'),
('diffusion b value number', '<i8'),
('gradient orientation number', '<i8'),
('contrast type', 'S30'),
('diffusion anisotropy type', 'S30'),
('diffusion', '<f8', (3, )), ('label type', '<i8')])
img.shape = (80, 80, 10)
self.libs.nibabel.load.return_value = img
self.libs.hasDependency.return_value = True
