def test_Threshold_outputs():
output_map = dict(activation_forced=dict(),
cluster_forming_thr=dict(),
n_clusters=dict(),
pre_topo_fdr_map=dict(),
pre_topo_n_clusters=dict(),
thresholded_map=dict(),
)
outputs = Threshold.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_Threshold_inputs():
input_map = dict(contrast_index=dict(mandatory=True,
),
extent_fdr_p_threshold=dict(usedefault=True,
),
extent_threshold=dict(usedefault=True,
),
force_activation=dict(usedefault=True,
),
height_threshold=dict(usedefault=True,
),
height_threshold_type=dict(usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
matlab_cmd=dict(),
mfile=dict(usedefault=True,
),
paths=dict(),
spm_mat_file=dict(copyfile=True,
mandatory=True,
),
stat_image=dict(copyfile=False,
mandatory=True,
),
use_fwe_correction=dict(usedefault=True,
),
use_mcr=dict(),
use_topo_fdr=dict(usedefault=True,
),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
)
inputs = Threshold.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
output_names=['out_path'],
function=plot_stat_maps),
name='plot_contrasts',
iterfield=['thresh'])
# input: plot data with set of different thresholds:
plot_contrasts.inputs.thresh = [None, 1, 2, 3]
# set expected thread and memory usage for the node:
plot_contrasts.interface.num_threads = 1
plot_contrasts.interface.mem_gb = 0.2
# ======================================================================
# DEFINE NODE: THRESHOLD
# ======================================================================
# function: Topological FDR thresholding based on cluster extent/size.
# Smoothness is estimated from GLM residuals but is assumed to be the
# same for all of the voxels.
thresh = Node(Threshold(), name="thresh")
# input: whether to use FWE (Bonferroni) correction for initial threshold
# (a boolean, nipype default value: True):
thresh.inputs.use_fwe_correction = False
# input: whether to use FDR over cluster extent probabilities (boolean)
thresh.inputs.use_topo_fdr = True
# input: value for initial thresholding (defining clusters):
thresh.inputs.height_threshold = 0.05
# input: is the cluster forming threshold a stat value or p-value?
# ('p-value' or 'stat', nipype default value: p-value):
thresh.inputs.height_threshold_type = 'p-value'
# input: which contrast in the SPM.mat to use (an integer):
thresh.inputs.contrast_index = 1
# input: p threshold on FDR corrected cluster size probabilities (float):
thresh.inputs.extent_fdr_p_threshold = 0.05
# input: minimum cluster size in voxels (an integer, default = 0):
