def test_FactorialDesign_outputs():
output_map = dict(spm_mat_file=dict(), )
outputs = FactorialDesign.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_FactorialDesign_outputs():
output_map = dict(spm_mat_file=dict(),
)
outputs = FactorialDesign.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_FactorialDesign_inputs():
input_map = dict(
covariates=dict(field='cov', ),
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', ),
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'],
),
use_implicit_threshold=dict(field='masking.im', ),
use_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
)
inputs = FactorialDesign.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_FactorialDesign_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
paths=dict(),
use_implicit_threshold=dict(field='masking.im',
),
global_calc_mean=dict(field='globalc.g_mean',
xor=['global_calc_omit', 'global_calc_values'],
),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
threshold_mask_absolute=dict(field='masking.tm.tma.athresh',
xor=['threshold_mask_none', 'threshold_mask_relative'],
),
use_mcr=dict(),
threshold_mask_none=dict(field='masking.tm.tm_none',
xor=['threshold_mask_absolute', 'threshold_mask_relative'],
),
covariates=dict(field='cov',
),
global_calc_omit=dict(field='globalc.g_omit',
xor=['global_calc_mean', 'global_calc_values'],
),
matlab_cmd=dict(),
global_calc_values=dict(field='globalc.g_user.global_uval',
xor=['global_calc_mean', 'global_calc_omit'],
),
mfile=dict(usedefault=True,
),
no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no',
),
explicit_mask_file=dict(field='masking.em',
),
threshold_mask_relative=dict(field='masking.tm.tmr.rthresh',
xor=['threshold_mask_absolute', 'threshold_mask_none'],
),
spm_mat_dir=dict(field='dir',
),
global_normalization=dict(field='globalm.glonorm',
),
)
inputs = FactorialDesign.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
