def test_XFibres5_outputs():
output_map = dict(dyads=dict(),
fsamples=dict(),
mean_S0samples=dict(),
mean_dsamples=dict(),
mean_fsamples=dict(),
mean_tausamples=dict(),
phsamples=dict(),
thsamples=dict(),
)
outputs = XFibres5.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_XFibres5_outputs():
output_map = dict(
dyads=dict(),
fsamples=dict(),
mean_S0samples=dict(),
mean_dsamples=dict(),
mean_fsamples=dict(),
mean_tausamples=dict(),
phsamples=dict(),
thsamples=dict(),
)
outputs = XFibres5.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_XFibres5_inputs():
input_map = dict(
all_ard=dict(argstr="--allard", xor=("no_ard", "all_ard")),
args=dict(argstr="%s"),
burn_in=dict(argstr="--burnin=%d"),
burn_in_no_ard=dict(argstr="--burninnoard=%d"),
bvals=dict(argstr="--bvals=%s", mandatory=True),
bvecs=dict(argstr="--bvecs=%s", mandatory=True),
cnlinear=dict(argstr="--cnonlinear", xor=("no_spat", "non_linear", "cnlinear")),
dwi=dict(argstr="--data=%s", mandatory=True),
environ=dict(nohash=True, usedefault=True),
f0_ard=dict(argstr="--f0 --ardf0", xor=["f0_noard", "f0_ard", "all_ard"]),
f0_noard=dict(argstr="--f0", xor=["f0_noard", "f0_ard"]),
force_dir=dict(argstr="--forcedir", usedefault=True),
fudge=dict(argstr="--fudge=%d"),
gradnonlin=dict(argstr="--gradnonlin=%s"),
ignore_exception=dict(nohash=True, usedefault=True),
logdir=dict(argstr="--logdir=%s", usedefault=True),
mask=dict(argstr="--mask=%s", mandatory=True),
model=dict(argstr="--model=%d"),
n_fibres=dict(argstr="--nfibres=%d"),
n_jumps=dict(argstr="--njumps=%d"),
no_ard=dict(argstr="--noard", xor=("no_ard", "all_ard")),
no_spat=dict(argstr="--nospat", xor=("no_spat", "non_linear", "cnlinear")),
non_linear=dict(argstr="--nonlinear", xor=("no_spat", "non_linear", "cnlinear")),
output_type=dict(),
rician=dict(argstr="--rician"),
sample_every=dict(argstr="--sampleevery=%d"),
seed=dict(argstr="--seed=%d"),
terminal_output=dict(nohash=True),
update_proposal_every=dict(argstr="--updateproposalevery=%d"),
)
inputs = XFibres5.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_XFibres5_inputs():
input_map = dict(
all_ard=dict(
argstr='--allard',
xor=('no_ard', 'all_ard'),
),
args=dict(argstr='%s', ),
burn_in=dict(argstr='--burnin=%d', ),
burn_in_no_ard=dict(argstr='--burninnoard=%d', ),
bvals=dict(
argstr='--bvals=%s',
mandatory=True,
),
bvecs=dict(
argstr='--bvecs=%s',
mandatory=True,
),
cnlinear=dict(
argstr='--cnonlinear',
xor=('no_spat', 'non_linear', 'cnlinear'),
),
dwi=dict(
argstr='--data=%s',
mandatory=True,
),
environ=dict(
nohash=True,
usedefault=True,
),
f0_ard=dict(
argstr='--f0 --ardf0',
xor=['f0_noard', 'f0_ard', 'all_ard'],
),
f0_noard=dict(
argstr='--f0',
xor=['f0_noard', 'f0_ard'],
),
force_dir=dict(
argstr='--forcedir',
usedefault=True,
),
fudge=dict(argstr='--fudge=%d', ),
gradnonlin=dict(argstr='--gradnonlin=%s', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
logdir=dict(
argstr='--logdir=%s',
usedefault=True,
),
mask=dict(
argstr='--mask=%s',
mandatory=True,
),
model=dict(argstr='--model=%d', ),
n_fibres=dict(
argstr='--nfibres=%d',
mandatory=True,
usedefault=True,
),
n_jumps=dict(argstr='--njumps=%d', ),
no_ard=dict(
argstr='--noard',
xor=('no_ard', 'all_ard'),
),
no_spat=dict(
argstr='--nospat',
xor=('no_spat', 'non_linear', 'cnlinear'),
),
non_linear=dict(
argstr='--nonlinear',
xor=('no_spat', 'non_linear', 'cnlinear'),
),
output_type=dict(),
rician=dict(argstr='--rician', ),
sample_every=dict(argstr='--sampleevery=%d', ),
seed=dict(argstr='--seed=%d', ),
terminal_output=dict(nohash=True, ),
update_proposal_every=dict(argstr='--updateproposalevery=%d', ),
)
inputs = XFibres5.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
