def test_ThresholdStatistics_inputs():
input_map = dict(contrast_index=dict(mandatory=True,
),
extent_threshold=dict(usedefault=True,
),
height_threshold=dict(mandatory=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_mcr=dict(),
use_v8struct=dict(min_ver='8',
usedefault=True,
),
)
inputs = ThresholdStatistics.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_ThresholdStatistics_inputs():
input_map = dict(
contrast_index=dict(mandatory=True, ),
extent_threshold=dict(usedefault=True, ),
height_threshold=dict(mandatory=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_mcr=dict(),
use_v8struct=dict(
min_ver='8',
usedefault=True,
),
)
inputs = ThresholdStatistics.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_ThresholdStatistics_outputs():
output_map = dict(clusterwise_P_FDR=dict(),
clusterwise_P_RF=dict(),
voxelwise_P_Bonf=dict(),
voxelwise_P_FDR=dict(),
voxelwise_P_RF=dict(),
voxelwise_P_uncor=dict(),
)
outputs = ThresholdStatistics.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_ThresholdStatistics_outputs():
output_map = dict(
clusterwise_P_FDR=dict(),
clusterwise_P_RF=dict(),
voxelwise_P_Bonf=dict(),
voxelwise_P_FDR=dict(),
voxelwise_P_RF=dict(),
voxelwise_P_uncor=dict(),
)
outputs = ThresholdStatistics.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), 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):
thresh.inputs.extent_threshold = 0
# set expected thread and memory usage for the node:
thresh.interface.num_threads = 1
thresh.interface.mem_gb = 0.2
# ======================================================================
# DEFINE NODE: THRESHOLD STATISTICS
# ======================================================================
# function: Given height and cluster size threshold calculate
# theoretical probabilities concerning false positives
thresh_stat = Node(ThresholdStatistics(), name="thresh_stat")
# input: which contrast in the SPM.mat to use (an integer):
thresh_stat.inputs.contrast_index = 1
# ======================================================================
# CREATE DATASINK NODE (OUTPUT STREAM):
# ======================================================================
# create a node of the function:
l1datasink = Node(DataSink(), name='datasink')
# assign the path to the base directory:
l1datasink.inputs.base_directory = opj(path_root, 'l1pipeline')
# create a list of substitutions to adjust the file paths of datasink:
substitutions = [('_subject_id_', '')]
# assign the substitutions to the datasink command:
l1datasink.inputs.substitutions = substitutions
# determine whether to store output in parameterized form:
l1datasink.inputs.parameterization = True
