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