def test_EstimateContrast_inputs():
input_map = dict(beta_images=dict(copyfile=False,
mandatory=True,
),
contrasts=dict(mandatory=True,
),
group_contrast=dict(xor=['use_derivs'],
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
matlab_cmd=dict(),
mfile=dict(usedefault=True,
),
paths=dict(),
residual_image=dict(copyfile=False,
mandatory=True,
),
spm_mat_file=dict(copyfile=True,
field='spmmat',
mandatory=True,
),
use_derivs=dict(xor=['group_contrast'],
),
use_mcr=dict(),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
)
inputs = EstimateContrast.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_EstimateContrast_outputs():
output_map = dict(con_images=dict(),
ess_images=dict(),
spmF_images=dict(),
spmT_images=dict(),
spm_mat_file=dict(),
)
outputs = EstimateContrast.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
# ======================================================================
# DEFINE NODE: ESTIMATE MODEL (ESTIMATE THE PARAMETERS OF THE MODEL)
# ======================================================================
# function: use spm_spm to estimate the parameters of a model
l1estimate = Node(EstimateModel(), name="l1estimate")
# input: (a dictionary with keys which are 'Classical' or 'Bayesian2'
# or 'Bayesian' and with values which are any value)
l1estimate.inputs.estimation_method = {'Classical': 1}
# set expected thread and memory usage for the node:
l1estimate.interface.num_threads = 1
l1estimate.interface.mem_gb = 2
# ======================================================================
# DEFINE NODE: ESTIMATE CONTRASTS (ESTIMATES THE CONTRASTS)
# ======================================================================
# function: use spm_contrasts to estimate contrasts of interest
l1contrasts = Node(EstimateContrast(), name="l1contrasts")
# input: list of contrasts with each contrast being a list of the form:
# [('name', 'stat', [condition list], [weight list], [session list])]:
# l1contrasts.inputs.contrasts = l1contrasts_list
# node input: overwrite previous results:
l1contrasts.overwrite = True
# set expected thread and memory usage for the node:
l1contrasts.interface.num_threads = 1
l1contrasts.interface.mem_gb = 1.5
# ======================================================================
# DEFINE NODE: FUNCTION TO PLOT CONTRASTS
# ======================================================================
plot_contrasts = MapNode(Function(input_names=['anat', 'stat_map', 'thresh'],
output_names=['out_path'],
function=plot_stat_maps),
name='plot_contrasts',
