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