def test_TwoSampleTTestDesign_outputs():
output_map = dict(spm_mat_file=dict())
outputs = TwoSampleTTestDesign.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_TwoSampleTTestDesign_outputs():
output_map = dict(spm_mat_file=dict(), )
outputs = TwoSampleTTestDesign.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_TwoSampleTTestDesign_inputs():
input_map = dict(
covariates=dict(field='cov', ),
dependent=dict(field='des.t2.dept', ),
explicit_mask_file=dict(field='masking.em', ),
global_calc_mean=dict(
field='globalc.g_mean',
xor=['global_calc_omit', 'global_calc_values'],
),
global_calc_omit=dict(
field='globalc.g_omit',
xor=['global_calc_mean', 'global_calc_values'],
),
global_calc_values=dict(
field='globalc.g_user.global_uval',
xor=['global_calc_mean', 'global_calc_omit'],
),
global_normalization=dict(field='globalm.glonorm', ),
group1_files=dict(
field='des.t2.scans1',
mandatory=True,
),
group2_files=dict(
field='des.t2.scans2',
mandatory=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
matlab_cmd=dict(),
mfile=dict(usedefault=True, ),
no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no', ),
paths=dict(),
spm_mat_dir=dict(field='dir', ),
threshold_mask_absolute=dict(
field='masking.tm.tma.athresh',
xor=['threshold_mask_none', 'threshold_mask_relative'],
),
threshold_mask_none=dict(
field='masking.tm.tm_none',
xor=['threshold_mask_absolute', 'threshold_mask_relative'],
),
threshold_mask_relative=dict(
field='masking.tm.tmr.rthresh',
xor=['threshold_mask_absolute', 'threshold_mask_none'],
),
unequal_variance=dict(field='des.t2.variance', ),
use_implicit_threshold=dict(field='masking.im', ),
use_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
)
inputs = TwoSampleTTestDesign.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_TwoSampleTTestDesign_inputs():
input_map = dict(use_v8struct=dict(min_ver='8',
usedefault=True,
),
matlab_cmd=dict(),
explicit_mask_file=dict(field='masking.em',
),
paths=dict(),
unequal_variance=dict(field='des.t2.variance',
),
covariates=dict(field='cov',
),
mfile=dict(usedefault=True,
),
threshold_mask_absolute=dict(field='masking.tm.tma.athresh',
xor=['threshold_mask_none', 'threshold_mask_relative'],
),
global_calc_omit=dict(field='globalc.g_omit',
xor=['global_calc_mean', 'global_calc_values'],
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
use_implicit_threshold=dict(field='masking.im',
),
global_calc_mean=dict(field='globalc.g_mean',
xor=['global_calc_omit', 'global_calc_values'],
),
dependent=dict(field='des.t2.dept',
),
group1_files=dict(field='des.t2.scans1',
mandatory=True,
),
group2_files=dict(field='des.t2.scans2',
mandatory=True,
),
threshold_mask_none=dict(field='masking.tm.tm_none',
xor=['threshold_mask_absolute', 'threshold_mask_relative'],
),
use_mcr=dict(),
spm_mat_dir=dict(field='dir',
),
global_normalization=dict(field='globalm.glonorm',
),
no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no',
),
global_calc_values=dict(field='globalc.g_user.global_uval',
xor=['global_calc_mean', 'global_calc_omit'],
),
threshold_mask_relative=dict(field='masking.tm.tmr.rthresh',
xor=['threshold_mask_absolute', 'threshold_mask_none'],
),
)
inputs = TwoSampleTTestDesign.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_TwoSampleTTestDesign_inputs():
input_map = dict(
covariates=dict(field="cov"),
dependent=dict(field="des.t2.dept"),
explicit_mask_file=dict(field="masking.em"),
global_calc_mean=dict(field="globalc.g_mean", xor=["global_calc_omit", "global_calc_values"]),
global_calc_omit=dict(field="globalc.g_omit", xor=["global_calc_mean", "global_calc_values"]),
global_calc_values=dict(field="globalc.g_user.global_uval", xor=["global_calc_mean", "global_calc_omit"]),
global_normalization=dict(field="globalm.glonorm"),
group1_files=dict(field="des.t2.scans1", mandatory=True),
group2_files=dict(field="des.t2.scans2", mandatory=True),
ignore_exception=dict(nohash=True, usedefault=True),
matlab_cmd=dict(),
mfile=dict(usedefault=True),
no_grand_mean_scaling=dict(field="globalm.gmsca.gmsca_no"),
paths=dict(),
spm_mat_dir=dict(field="dir"),
threshold_mask_absolute=dict(
field="masking.tm.tma.athresh", xor=["threshold_mask_none", "threshold_mask_relative"]
),
threshold_mask_none=dict(
field="masking.tm.tm_none", xor=["threshold_mask_absolute", "threshold_mask_relative"]
),
threshold_mask_relative=dict(
field="masking.tm.tmr.rthresh", xor=["threshold_mask_absolute", "threshold_mask_none"]
),
unequal_variance=dict(field="des.t2.variance"),
use_implicit_threshold=dict(field="masking.im"),
use_mcr=dict(),
use_v8struct=dict(min_ver="8", usedefault=True),
)
inputs = TwoSampleTTestDesign.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
