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7 | 7 | import numpy as np
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8 | 8 |
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9 | 9 |
|
10 |
| -def test_artifactdetect(): |
11 |
| - input_map = dict(intersect_mask=dict(), |
12 |
| - mask_file=dict(), |
13 |
| - mask_threshold=dict(), |
14 |
| - mask_type=dict(), |
15 |
| - norm_threshold=dict(), |
16 |
| - parameter_source=dict(mandatory=True,), |
17 |
| - realigned_files=dict(mandatory=True,), |
18 |
| - realignment_parameters=dict(), |
19 |
| - rotation_threshold=dict(), |
20 |
| - translation_threshold=dict(), |
21 |
| - use_differences=dict(usedefault=True,), |
22 |
| - use_norm=dict(usedefault=True,), |
23 |
| - zintensity_threshold=dict(), |
24 |
| - ) |
25 |
| - instance = ra.ArtifactDetect() |
26 |
| - for key, metadata in input_map.items(): |
27 |
| - for metakey, value in metadata.items(): |
28 |
| - yield assert_equal, getattr(instance.inputs.traits()[key], metakey), value |
29 |
| - |
30 |
| - |
31 |
| -def test_stimuluscorrelation(): |
32 |
| - input_map = dict(concatenated_design=dict(mandatory=True,), |
33 |
| - intensity_values=dict(mandatory=True,), |
34 |
| - realignment_parameters=dict(mandatory=True,), |
35 |
| - spm_mat_file=dict(mandatory=True,), |
36 |
| - ) |
37 |
| - instance = ra.StimulusCorrelation() |
38 |
| - for key, metadata in input_map.items(): |
39 |
| - for metakey, value in metadata.items(): |
40 |
| - yield assert_equal, getattr(instance.inputs.traits()[key], metakey), value |
41 |
| - |
42 |
| - |
43 | 10 | def test_ad_init():
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44 | 11 | ad = ra.ArtifactDetect(use_differences=[True, False])
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45 | 12 | yield assert_true, ad.inputs.use_differences[0]
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