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import unittest |
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from mock import Mock |
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3
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from datetime import datetime, timedelta |
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4
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from tests.test_basefile import BaseFileTests |
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6
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class ParrecTests(BaseFileTests): |
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9
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def setUp(self): |
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super(ParrecTests, self).setUp() |
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11
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self.libs = Mock() |
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12
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self.dependencies.getLibraries.return_value = self.libs |
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self.setupNibabel() |
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14
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from niprov.parrec import ParrecFile |
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self.constructor = ParrecFile |
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self.file = ParrecFile(self.path, dependencies=self.dependencies) |
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def test_Gets_basic_info_from_nibabel_and_returns_it(self): |
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out = self.file.inspect() |
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self.assertEqual(out['subject'], 'John Doeish') |
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self.assertEqual(out['protocol'], 'T1 SENSE') |
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self.assertEqual(out['acquired'], datetime(2014, 8, 5, 11, 27, 34)) |
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def test_Gets_dimensions(self): |
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out = self.file.inspect() |
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self.assertEqual(out['dimensions'], [80,80,10]) |
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27
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28
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def test_Gets_advanced_fields(self): |
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out = self.file.inspect() |
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self.assertEqual(out['technique'], 'T1TFE') |
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31
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self.assertEqual(out['repetition-time'], 4.364) |
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32
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self.assertEqual(out['field-of-view'], [130., 100., 154.375]) |
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33
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self.assertEqual(out['epi-factor'], 1) |
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34
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self.assertEqual(out['magnetization-transfer-contrast'], False) |
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35
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self.assertEqual(out['diffusion'], False) |
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36
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self.assertEqual(out['duration'], timedelta(seconds=65)) |
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self.assertEqual(out['subject-position'], 'Head First Supine') |
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self.assertEqual(out['water-fat-shift'], 1.117) |
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39
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# per-image |
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40
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self.assertEqual(out['slice-thickness'], 10.0) |
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self.assertEqual(out['slice-orientation'], 1) |
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42
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self.assertEqual(out['echo-time'], 2.0800000000000001) |
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self.assertEqual(out['flip-angle'], 8.0) |
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self.assertEqual(out['inversion-time'], 0.0) |
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def test_getProtocolFields(self): |
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protocol = self.file.getProtocolFields() |
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self.assertIn('repetition-time', protocol) |
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self.assertIn('echo-time', protocol) |
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def setupNibabel(self): |
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import numpy |
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img = Mock() |
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54
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img.header.general_info = { |
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'acq_nr': 6, |
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'angulation': numpy.array([-1.979, 0.546, 0.019]), |
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'diffusion': 0, |
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'diffusion_echo_time': 0.0, |
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'dyn_scan': 0, |
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60
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'epi_factor': 1, |
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'exam_date': '2014.08.05 / 11:27:34', |
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'exam_name': 'test', |
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'flow_compensation': 0, |
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64
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'fov': numpy.array([ 130. , 100. , 154.375]), |
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65
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'max_cardiac_phases': 1, |
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66
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'max_diffusion_values': 1, |
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'max_dynamics': 1, |
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'max_echoes': 1, |
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'max_gradient_orient': 1, |
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'max_mixes': 1, |
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'max_slices': 10, |
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'mtc': 0, |
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'nr_label_types': 0, |
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'off_center': numpy.array([-18.805, 22.157, -17.977]), |
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'patient_name': 'John Doeish', |
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'patient_position': 'Head First Supine', |
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'phase_enc_velocity': numpy.array([ 0., 0., 0.]), |
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'prep_direction': 'Right-Left', |
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'presaturation': 0, |
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80
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'protocol_name': 'T1 SENSE', |
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'recon_nr': 1, |
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'repetition_time': 4.364, |
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'scan_duration': 65.0, |
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84
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'scan_mode': '3D', |
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85
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'scan_resolution': numpy.array([76, 62]), |
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86
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'series_type': 'Image MRSERIES', |
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'spir': 0, |
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'tech': 'T1TFE', |
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'water_fat_shift': 1.117} |
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90
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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), |
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91
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(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), |
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92
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(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)], |
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93
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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')]) |
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img.shape = (80,80,10) |
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self.libs.nibabel.load.return_value = img |
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self.libs.hasDependency.return_value = True |
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100
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101
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ilogue /
niprov
