def test_FilterRegressor_outputs(): output_map = dict(out_file=dict(), ) outputs = FilterRegressor.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_FilterRegressor_outputs(): output_map = dict(out_file=dict()) outputs = FilterRegressor.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_FilterRegressor_inputs(): input_map = dict( args=dict(argstr='%s', ), design_file=dict( argstr='-d %s', mandatory=True, position=3, ), environ=dict( nohash=True, usedefault=True, ), filter_all=dict( argstr="-f '%s'", mandatory=True, position=4, xor=['filter_columns'], ), filter_columns=dict( argstr="-f '%s'", mandatory=True, position=4, xor=['filter_all'], ), ignore_exception=dict( nohash=True, usedefault=True, ), in_file=dict( argstr='-i %s', mandatory=True, position=1, ), mask=dict(argstr='-m %s', ), out_file=dict( argstr='-o %s', genfile=True, hash_files=False, position=2, ), out_vnscales=dict(argstr='--out_vnscales', ), output_type=dict(), terminal_output=dict( mandatory=True, nohash=True, ), var_norm=dict(argstr='--vn', ), ) inputs = FilterRegressor.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_FilterRegressor_inputs(): input_map = dict(ignore_exception=dict(nohash=True, usedefault=True, ), out_file=dict(hash_files=False, genfile=True, position=2, argstr='-o %s', ), filter_all=dict(mandatory=True, position=4, xor=['filter_columns'], argstr="-f '%s'", ), out_vnscales=dict(argstr='--out_vnscales', ), args=dict(argstr='%s', ), mask=dict(argstr='-m %s', ), terminal_output=dict(mandatory=True, nohash=True, ), environ=dict(nohash=True, usedefault=True, ), in_file=dict(position=1, mandatory=True, argstr='-i %s', ), output_type=dict(), filter_columns=dict(xor=['filter_all'], position=4, mandatory=True, argstr="-f '%s'", ), var_norm=dict(argstr='--vn', ), design_file=dict(position=3, mandatory=True, argstr='-d %s', ), ) inputs = FilterRegressor.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_FilterRegressor_inputs(): input_map = dict( args=dict(argstr="%s"), design_file=dict(argstr="-d %s", mandatory=True, position=3), environ=dict(nohash=True, usedefault=True), filter_all=dict(argstr="-f '%s'", mandatory=True, position=4, xor=["filter_columns"]), filter_columns=dict(argstr="-f '%s'", mandatory=True, position=4, xor=["filter_all"]), ignore_exception=dict(nohash=True, usedefault=True), in_file=dict(argstr="-i %s", mandatory=True, position=1), mask=dict(argstr="-m %s"), out_file=dict(argstr="-o %s", genfile=True, hash_files=False, position=2), out_vnscales=dict(argstr="--out_vnscales"), output_type=dict(), terminal_output=dict(nohash=True), var_norm=dict(argstr="--vn"), ) inputs = FilterRegressor.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
glob.glob(curdir + '/*' + curfunc.split('task-')[1].split('_')[0] + '*' + curfunc.split('run-')[1].split('_')[0] + '*' + '*AROMAnoiseICs.csv')[0], delimiter=',').astype('int')) if (not os.path.isfile(outfile) or overwrite) or (not os.path.isfile(tmpAROMA) and overwrite): FilterRegressor( design_file=glob.glob( curdir + '/*' + curfunc.split('task-')[1].split('_')[0] + '*' + curfunc.split('run-')[1].split('_')[0] + '*' + '*MELODIC*.tsv')[0], filter_columns=list( np.loadtxt( glob.glob(curdir + '/*' + curfunc.split('task-')[1].split('_')[0] + '*' + curfunc.split('run-')[1].split('_')[0] + '*' + '*AROMAnoiseICs.csv')[0], delimiter=',').astype('int')), in_file=curfunc, mask=curmask, out_file=tmpAROMA).run() if not os.path.isfile(tmpAROMAconf): if not os.path.isfile(tmpAROMAwm) or not os.path.isfile( tmpAROMAcsf): from nipype.interfaces.fsl.maths import Threshold from nipype.interfaces.fsl.utils import ImageMeants Threshold( in_file=cursegm,