def test_SUSAN_outputs(): output_map = dict(smoothed_file=dict(), ) outputs = SUSAN.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_SUSAN_inputs(): input_map = dict( args=dict(argstr='%s', ), brightness_threshold=dict( argstr='%.10f', mandatory=True, position=2, ), dimension=dict( argstr='%d', position=4, usedefault=True, ), environ=dict( nohash=True, usedefault=True, ), fwhm=dict( argstr='%.10f', mandatory=True, position=3, ), ignore_exception=dict( nohash=True, usedefault=True, ), in_file=dict( argstr='%s', mandatory=True, position=1, ), out_file=dict( argstr='%s', genfile=True, hash_files=False, position=-1, ), output_type=dict(), terminal_output=dict( mandatory=True, nohash=True, ), usans=dict( argstr='', position=6, usedefault=True, ), use_median=dict( argstr='%d', position=5, usedefault=True, ), ) inputs = SUSAN.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_SUSAN_inputs(): input_map = dict(args=dict(argstr='%s', ), brightness_threshold=dict(argstr='%.10f', mandatory=True, position=2, ), dimension=dict(argstr='%d', position=4, usedefault=True, ), environ=dict(nohash=True, usedefault=True, ), fwhm=dict(argstr='%.10f', mandatory=True, position=3, ), ignore_exception=dict(nohash=True, usedefault=True, ), in_file=dict(argstr='%s', mandatory=True, position=1, ), out_file=dict(argstr='%s', genfile=True, hash_files=False, position=-1, ), output_type=dict(), terminal_output=dict(mandatory=True, nohash=True, ), usans=dict(argstr='', position=6, usedefault=True, ), use_median=dict(argstr='%d', position=5, usedefault=True, ), ) inputs = SUSAN.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
func_data = func_nifti1.get_data() func_data = func_data.astype(float) brain_values = where(func_data > 0) median_thresh = median(brain_values) bright_thresh = 0.75 * median_thresh return (bright_thresh) brightthresh_filt = Node(name='brightthresh_filt', interface=Function(input_names=['func'], output_names=['bright_thresh'], function=brightthresh)) smooth_filt = Node(SUSAN(fwhm=fwhm), name='smooth_filt') brightthresh_orig = Node(name='brightthresh_orig', interface=Function(input_names=['func'], output_names=['bright_thresh'], function=brightthresh)) smooth_orig = Node(SUSAN(fwhm=fwhm), name='smooth_orig') # In[8]: ## Preprocessing Workflow # workflowname.connect([(node1,node2,[('node1output','node2input')]), # (node2,node3,[('node2output','node3input')]) # ])