def test_s3datagrabber_communication(): dg = nio.S3DataGrabber(infields=['subj_id', 'run_num'], outfields=['func', 'struct']) dg.inputs.anon = True dg.inputs.bucket = 'openfmri' dg.inputs.bucket_path = 'ds001/' tempdir = mkdtemp() dg.inputs.local_directory = tempdir dg.inputs.sort_filelist = True dg.inputs.template = '*' dg.inputs.field_template = dict(func='%s/BOLD/task001_%s/bold.nii.gz', struct='%s/anatomy/highres001_brain.nii.gz') dg.inputs.subj_id = ['sub001', 'sub002'] dg.inputs.run_num = ['run001', 'run003'] dg.inputs.template_args = dg.inputs.template_args = dict( func=[['subj_id', 'run_num']], struct=[['subj_id']]) res = dg.run() func_outfiles = res.outputs.func struct_outfiles = res.outputs.struct # check for all files yield assert_true, '/sub001/BOLD/task001_run001/bold.nii.gz' in func_outfiles[0] yield assert_true, os.path.exists(func_outfiles[0]) yield assert_true, '/sub001/anatomy/highres001_brain.nii.gz' in struct_outfiles[0] yield assert_true, os.path.exists(struct_outfiles[0]) yield assert_true, '/sub002/BOLD/task001_run003/bold.nii.gz' in func_outfiles[1] yield assert_true, os.path.exists(func_outfiles[1]) yield assert_true, '/sub002/anatomy/highres001_brain.nii.gz' in struct_outfiles[1] yield assert_true, os.path.exists(struct_outfiles[1]) shutil.rmtree(tempdir)
def test_s3datagrabber_communication(tmpdir): dg = nio.S3DataGrabber(infields=["subj_id", "run_num"], outfields=["func", "struct"]) dg.inputs.anon = True dg.inputs.bucket = "openfmri" dg.inputs.bucket_path = "ds001/" dg.inputs.local_directory = tmpdir.strpath dg.inputs.sort_filelist = True dg.inputs.template = "*" dg.inputs.field_template = dict( func="%s/BOLD/task001_%s/bold.nii.gz", struct="%s/anatomy/highres001_brain.nii.gz", ) dg.inputs.subj_id = ["sub001", "sub002"] dg.inputs.run_num = ["run001", "run003"] dg.inputs.template_args = dict(func=[["subj_id", "run_num"]], struct=[["subj_id"]]) res = dg.run() func_outfiles = res.outputs.func struct_outfiles = res.outputs.struct # check for all files assert (os.path.join(dg.inputs.local_directory, "/sub001/BOLD/task001_run001/bold.nii.gz") in func_outfiles[0]) assert os.path.exists(func_outfiles[0]) assert (os.path.join(dg.inputs.local_directory, "/sub001/anatomy/highres001_brain.nii.gz") in struct_outfiles[0]) assert os.path.exists(struct_outfiles[0]) assert (os.path.join(dg.inputs.local_directory, "/sub002/BOLD/task001_run003/bold.nii.gz") in func_outfiles[1]) assert os.path.exists(func_outfiles[1]) assert (os.path.join(dg.inputs.local_directory, "/sub002/anatomy/highres001_brain.nii.gz") in struct_outfiles[1]) assert os.path.exists(struct_outfiles[1])
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.fsl as fsl import nipype.interfaces.io as io #Wraps command **bet** my_fsl_BET = pe.Node(interface=fsl.BET(), name='my_fsl_BET', iterfield=['']) #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["out_file, func"]), name='my_io_S3DataGrabber') #Generic datasink module to store structured outputs my_io_DataSink = pe.Node(interface=io.DataSink(), name='my_io_DataSink', iterfield=['']) #Wraps command **epi_reg** my_fsl_EpiReg = pe.Node(interface=fsl.EpiReg(), name='my_fsl_EpiReg', iterfield=['']) #Create a workflow to connect all those nodes analysisflow = nipype.Workflow('MyWorkflow') analysisflow.connect(my_io_S3DataGrabber, "out_file", my_fsl_BET, "in_file") analysisflow.connect(my_fsl_BET, "out_file", my_fsl_EpiReg, "t1_brain")
#This is a Nipype generator. Warning, here be dragons. import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl WorkingDirectory = "~/Porcupipelines/ThisStudy" #Generic datagrabber module that wraps around glob in an NodeHash_17c5c70 = pe.Node(io.S3DataGrabber(outfields=['outfiles']), name='NodeName_17c5c70') NodeHash_17c5c70.inputs.bucket = 'openneuro' NodeHash_17c5c70.inputs.sort_filelist = True NodeHash_17c5c70.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' NodeHash_17c5c70.inputs.anon = True NodeHash_17c5c70.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_17c5c70.inputs.local_directory = '/tmp' #Wraps command **bet** NodeHash_211a5f0 = pe.Node(interface=fsl.BET(), name='NodeName_211a5f0') #Generic datasink module to store structured outputs NodeHash_236ab50 = pe.Node(interface=io.DataSink(), name='NodeName_236ab50') NodeHash_236ab50.inputs.base_directory = '/tmp' #Create a workflow to connect all those nodes analysisflow = nipype.Workflow('MyWorkflow') analysisflow.connect(NodeHash_211a5f0, 'out_file', NodeHash_236ab50, 'BET_results') analysisflow.connect(NodeHash_17c5c70, 'outfiles', NodeHash_211a5f0, 'in_file')
import nipype.pipeline as pe import nipype.interfaces.utility as utility import nipype.interfaces.io as io import nipype.interfaces.afni as afni WorkingDirectory = "~/Porcupipelines/ThisStudy" #Basic interface class generates identity mappings NodeHash_24ff4a0 = pe.Node( utility.IdentityInterface(fields=['sub_id', 'run_id']), name='NodeName_24ff4a0') NodeHash_24ff4a0.inputs.run_id = ['run-1', 'run-2'] NodeHash_24ff4a0.iterables = [('sub_id', ['sub-01', 'sub-02'])] #Generic datagrabber module that wraps around glob in an NodeHash_1e88370 = pe.Node(io.S3DataGrabber(infields=['sub_id', 'run_id'], outfields=['func']), name='NodeName_1e88370') NodeHash_1e88370.inputs.bucket = 'openneuro' NodeHash_1e88370.inputs.sort_filelist = True NodeHash_1e88370.inputs.template = '%s/func/%s_task-simon_%s_bold.nii.gz' NodeHash_1e88370.inputs.anon = True NodeHash_1e88370.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_1e88370.inputs.local_directory = '/tmp' NodeHash_1e88370.inputs.template_args = dict( func=[['sub_id', 'sub_id', 'run_id']]) #Wraps command **3dvolreg** NodeHash_19153b0 = pe.MapNode(interface=afni.Volreg(), name='NodeName_19153b0', iterfield=['in_file']) NodeHash_19153b0.inputs.outputtype = 'NIFTI_GZ'
#This is a Nipype generator. Warning, here be dragons. import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import firstlevelhelpers import nipype.algorithms.modelgen as modelgen WorkingDirectory = "~/Porcupipelines/ThisStudy" #Generic datagrabber module that wraps around glob in an NodeHash_32c4e30 = pe.Node(io.S3DataGrabber(infields=['field_template'], outfields=['func', 'events']), name='NodeName_32c4e30') NodeHash_32c4e30.inputs.anon = True NodeHash_32c4e30.inputs.bucket = 'openneuro' NodeHash_32c4e30.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_32c4e30.inputs.local_directory = '/tmp' NodeHash_32c4e30.inputs.sort_filelist = True NodeHash_32c4e30.inputs.template = '*' NodeHash_32c4e30.inputs.template_args = dict(func=[['bold.nii.gz']], events=[['events.tsv']]) NodeHash_32c4e30.inputs.field_template = dict( func='sub-01/func/sub-01_task-simon_run-1_%s', events='sub-01/func/sub-01_task-simon_run-1_%s') #Wraps command **bet** NodeHash_3443a20 = pe.Node(interface=fsl.BET(), name='NodeName_3443a20') NodeHash_3443a20.inputs.frac = 0.3 NodeHash_3443a20.inputs.mask = True
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import nipype.interfaces.utility as utility #Generic datagrabber module that wraps around glob in an anat_from_openneuro = pe.Node(io.S3DataGrabber(outfields=["anat"]), name='anat_from_openneuro') anat_from_openneuro.inputs.bucket = 'openneuro' anat_from_openneuro.inputs.sort_filelist = True anat_from_openneuro.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' anat_from_openneuro.inputs.anon = True anat_from_openneuro.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' anat_from_openneuro.inputs.local_directory = '/tmp' #Wraps command **bet** brain_extraction = pe.Node(interface=fsl.BET(), name='brain_extraction', iterfield=['']) #Generic datagrabber module that wraps around glob in an func_from_openneuro = pe.Node(io.S3DataGrabber(outfields=["func"]), name='func_from_openneuro') func_from_openneuro.inputs.bucket = 'openneuro' func_from_openneuro.inputs.sort_filelist = True
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import nipype.interfaces.afni as afni #Generic datagrabber module that wraps around glob in an io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["outfiles"]), name='io_S3DataGrabber') io_S3DataGrabber.inputs.bucket = 'openneuro' io_S3DataGrabber.inputs.sort_filelist = True io_S3DataGrabber.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' io_S3DataGrabber.inputs.anon = True io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' io_S3DataGrabber.inputs.local_directory = '/tmp' #Wraps command **bet** fsl_BET = pe.Node(interface=fsl.BET(), name='fsl_BET', iterfield=['']) #Generic datasink module to store structured outputs io_DataSink = pe.Node(interface=io.DataSink(), name='io_DataSink', iterfield=['']) io_DataSink.inputs.base_directory = '/tmp' #Wraps command **3dAllineate**
import nipype import nipype.pipeline as pe import nipype.interfaces.utility as utility import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import firstlevelhelpers import nipype.algorithms.modelgen as modelgen WorkingDirectory = "~/Porcupipelines/ThisStudy" #Basic interface class generates identity mappings NodeHash_2c4dda0 = pe.Node(utility.IdentityInterface(fields=['sub_id']), name = 'NodeName_2c4dda0') NodeHash_2c4dda0.inputs.sub_id = ['sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16', 'sub-17', 'sub-18', 'sub-19', 'sub-20', 'sub-21'] #Generic datagrabber module that wraps around glob in an NodeHash_17173a00 = pe.MapNode(io.S3DataGrabber(infields=['field_template','sub_id'], outfields=['func','events','anat']), name = 'NodeName_17173a00', iterfield = ['sub_id']) NodeHash_17173a00.inputs.anon = True NodeHash_17173a00.inputs.bucket = 'openneuro' NodeHash_17173a00.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_17173a00.inputs.local_directory = '/tmp' NodeHash_17173a00.inputs.sort_filelist = True NodeHash_17173a00.inputs.template = '*' NodeHash_17173a00.inputs.template_args = dict(func=[['sub_id', 'sub_id']], events=[['sub_id', 'sub_id']], anat=[['sub_id', 'sub_id']]) NodeHash_17173a00.inputs.field_template = dict(func='%s/func/%s_task-simon_run-1_bold.nii.gz', events='%s/func/%s_task-simon_run-1_events.tsv', anat='%s/anat/%s_T1w.nii.gz') #Wraps command **bet** NodeHash_20af2180 = pe.MapNode(interface = fsl.BET(), name = 'NodeName_20af2180', iterfield = ['in_file']) NodeHash_20af2180.inputs.frac = 0.3 NodeHash_20af2180.inputs.robust = True #Wraps command **fast**
import nipype.algorithms.modelgen as modelgen WorkingDirectory = "~/Porcupipelines/ThisStudy" #Basic interface class generates identity mappings NodeHash_30ba470 = pe.Node(utility.IdentityInterface(fields=['sub_id']), name='NodeName_30ba470') NodeHash_30ba470.inputs.sub_id = [ 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16', 'sub-17', 'sub-18', 'sub-19', 'sub-20', 'sub-21' ] #Generic datagrabber module that wraps around glob in an NodeHash_34d5650 = pe.MapNode(io.S3DataGrabber( infields=['field_template', 'sub_id'], outfields=['func', 'events', 'anat']), name='NodeName_34d5650', iterfield=['sub_id']) NodeHash_34d5650.inputs.anon = True NodeHash_34d5650.inputs.bucket = 'openneuro' NodeHash_34d5650.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_34d5650.inputs.local_directory = '/tmp' NodeHash_34d5650.inputs.sort_filelist = True NodeHash_34d5650.inputs.template = '*' NodeHash_34d5650.inputs.template_args = dict(func=[['sub_id', 'sub_id']], events=[['sub_id', 'sub_id']], anat=[['sub_id', 'sub_id']]) NodeHash_34d5650.inputs.field_template = dict( func='%s/func/%s_task-simon_run-1_bold.nii.gz', events='%s/func/%s_task-simon_run-1_events.tsv',
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import nipype.interfaces.afni as afni import nipype.interfaces.ants as ants #Generic datagrabber module that wraps around glob in an io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["outfiles"]), name='io_S3DataGrabber') io_S3DataGrabber.inputs.bucket = 'openneuro' io_S3DataGrabber.inputs.sort_filelist = True io_S3DataGrabber.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' io_S3DataGrabber.inputs.anon = True io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' io_S3DataGrabber.inputs.local_directory = '/tmp' #Wraps command **bet** fsl_BET = pe.Node(interface=fsl.BET(), name='fsl_BET', iterfield=['']) #Wraps command **3dTshift** afni_TShift = pe.Node(interface=afni.TShift(), name='afni_TShift', iterfield=['']) #Wraps command **3dUnifize**
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(infields=["field_template, subj_id"], outfields=["outfiles"]), name = 'my_io_S3DataGrabber') my_io_S3DataGrabber.inputs.bucket = 'openneuro' my_io_S3DataGrabber.inputs.sort_filelist = True my_io_S3DataGrabber.inputs.template = '*' my_io_S3DataGrabber.inputs.anon = True my_io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' my_io_S3DataGrabber.inputs.local_directory = '/tmp' my_io_S3DataGrabber.inputs.template_args = {'anat': [['subj_id', 'subj_id']], 'func': [['subj_id', 'subj_id']]} my_io_S3DataGrabber.inputs.field_template = {'anat': '%s/anat/%s_T1w.nii.gz', 'func': '%s/func/%s_task-simon_run-1_bold.nii.gz'} my_io_S3DataGrabber.inputs.subj_id = sub-01 #Wraps command **bet** my_fsl_BET = pe.Node(interface = fsl.BET(), name='my_fsl_BET', iterfield = ['']) #Generic datasink module to store structured outputs my_io_DataSink = pe.Node(interface = io.DataSink(), name='my_io_DataSink', iterfield = ['']) my_io_DataSink.inputs.base_directory = '/tmp' #Create a workflow to connect all those nodes analysisflow = nipype.Workflow('MyWorkflow')
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["func, anat"]), name='my_io_S3DataGrabber') my_io_S3DataGrabber.inputs.bucket = 'openneuro' my_io_S3DataGrabber.inputs.sort_filelist = True my_io_S3DataGrabber.inputs.template = '' my_io_S3DataGrabber.inputs.anon = True my_io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' my_io_S3DataGrabber.inputs.local_directory = '/tmp' #Wraps command **bet** my_fsl_BET = pe.Node(interface=fsl.BET(), name='my_fsl_BET', iterfield=['']) #Generic datasink module to store structured outputs my_io_DataSink = pe.Node(interface=io.DataSink(), name='my_io_DataSink', iterfield=['']) my_io_DataSink.inputs.base_directory = '/tmp' #Wraps command **epi_reg** my_fsl_EpiReg = pe.Node(interface=fsl.EpiReg(),
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import nipype.algorithms.confounds as confounds import nipype.interfaces.utility as utility #Generic datagrabber module that wraps around glob in an DataFromOpenNeuro = pe.Node(io.S3DataGrabber( infields=["subj_id", "run_num", "field_template"], outfields=["func", "struct"]), name='DataFromOpenNeuro') DataFromOpenNeuro.inputs.bucket = 'openfmri' DataFromOpenNeuro.inputs.sort_filelist = True DataFromOpenNeuro.inputs.template = '*' DataFromOpenNeuro.inputs.anon = True DataFromOpenNeuro.inputs.bucket_path = 'ds001/' DataFromOpenNeuro.inputs.local_directory = '/tmp' DataFromOpenNeuro.inputs.field_template = dict( func='%s/BOLD/task001_%s/bold.nii.gz', struct='%s/anatomy/highres001_brain.nii.gz') DataFromOpenNeuro.inputs.template_args = dict(func=[['subj_id', 'run_num']], struct=[['subj_id']]) #Wraps command **slicetimer** SliceTimer = pe.MapNode(interface=fsl.SliceTimer(),
#This is a Nipype generator. Warning, here be dragons. import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl import nipype.algorithms.confounds as confounds WorkingDirectory = "~/Porcupipelines/ThisStudy" #Generic datagrabber module that wraps around glob in an NodeHash_30f69e0 = pe.Node(io.S3DataGrabber(outfields=['outfiles']), name='NodeName_30f69e0') NodeHash_30f69e0.inputs.bucket = 'openneuro' NodeHash_30f69e0.inputs.sort_filelist = True NodeHash_30f69e0.inputs.template = 'sub-01/func/sub-01_task-simon_run-1_bold.nii.gz' NodeHash_30f69e0.inputs.anon = True NodeHash_30f69e0.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_30f69e0.inputs.local_directory = '/tmp' #Wraps command **slicetimer** NodeHash_1d000c0 = pe.Node(interface=fsl.SliceTimer(), name='NodeName_1d000c0') #Wraps command **mcflirt** NodeHash_22f2e80 = pe.Node(interface=fsl.MCFLIRT(), name='NodeName_22f2e80') #Computes the time-course SNR for a time series NodeHash_50c02c0 = pe.Node(interface=confounds.TSNR(), name='NodeName_50c02c0') NodeHash_50c02c0.inputs.regress_poly = 3 #Wraps command **fslstats**
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["outfiles"]), name = 'my_io_S3DataGrabber') my_io_S3DataGrabber.inputs.bucket = 'openneuro' my_io_S3DataGrabber.inputs.sort_filelist = True my_io_S3DataGrabber.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' my_io_S3DataGrabber.inputs.anon = True my_io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' my_io_S3DataGrabber.inputs.local_directory = '/tmp' #Wraps command **bet** my_fsl_BET = pe.Node(interface = fsl.BET(), name='my_fsl_BET', iterfield = ['']) #Generic datasink module to store structured outputs my_io_DataSink = pe.Node(interface = io.DataSink(), name='my_io_DataSink', iterfield = ['']) my_io_DataSink.inputs.base_directory = './output_dir' #Create a workflow to connect all those nodes analysisflow = nipype.Workflow('MyWorkflow') analysisflow.connect(my_io_S3DataGrabber, "outfiles", my_fsl_BET, "in_file") analysisflow.connect(my_fsl_BET, "out_file", my_io_DataSink, "BET_results")
def test_s3datagrabber(): dg = nio.S3DataGrabber() yield assert_equal, dg.inputs.template, Undefined yield assert_equal, dg.inputs.local_directory, Undefined yield assert_equal, dg.inputs.template_args, {'outfiles': []}
#This is a Nipype generator. Warning, here be dragons. #!/usr/bin/env python import sys import nipype import nipype.pipeline as pe import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(outfields=["outfiles, func, anat"]), name = 'my_io_S3DataGrabber') my_io_S3DataGrabber.inputs.bucket = 'openneuro' my_io_S3DataGrabber.inputs.sort_filelist = True my_io_S3DataGrabber.inputs.template = 'sub-01/anat/sub-01_T1w.nii.gz' my_io_S3DataGrabber.inputs.anon = True my_io_S3DataGrabber.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' my_io_S3DataGrabber.inputs.local_directory = '/tmp' #Wraps command **bet** my_fsl_BET = pe.Node(interface = fsl.BET(), name='my_fsl_BET', iterfield = ['']) #Generic datasink module to store structured outputs my_io_DataSink = pe.Node(interface = io.DataSink(), name='my_io_DataSink', iterfield = ['']) my_io_DataSink.inputs.base_directory = '/tmp' #Wraps command **epi_reg** my_fsl_EpiReg = pe.Node(interface = fsl.EpiReg(), name='my_fsl_EpiReg', iterfield = ['']) #Generic datagrabber module that wraps around glob in an my_io_S3DataGrabber = pe.Node(io.S3DataGrabber(), name = 'my_io_S3DataGrabber')
def test_s3datagrabber(): dg = nio.S3DataGrabber() assert dg.inputs.template == Undefined assert dg.inputs.local_directory == Undefined assert dg.inputs.template_args == {'outfiles': []}
import sys import nipype import nipype.pipeline as pe import nipype.interfaces.utility as utility import nipype.interfaces.io as io import nipype.interfaces.fsl as fsl WorkingDirectory = "~/Porcupipelines/ThisStudy" #Basic interface class generates identity mappings NodeHash_1c35840 = pe.Node(utility.IdentityInterface(fields=['sub_id']), name='NodeName_1c35840') NodeHash_1c35840.iterables = [('sub_id', ['sub-01', 'sub-02'])] #Generic datagrabber module that wraps around glob in an NodeHash_1d9b790 = pe.Node(io.S3DataGrabber(infields=['sub_id'], outfields=['anat']), name='NodeName_1d9b790') NodeHash_1d9b790.inputs.bucket = 'openneuro' NodeHash_1d9b790.inputs.sort_filelist = True NodeHash_1d9b790.inputs.template = '%s/anat/%s_T1w.nii.gz' NodeHash_1d9b790.inputs.anon = True NodeHash_1d9b790.inputs.bucket_path = 'ds000101/ds000101_R2.0.0/uncompressed/' NodeHash_1d9b790.inputs.local_directory = '/tmp' NodeHash_1d9b790.inputs.template_args = dict(anat=[['sub_id', 'sub_id']]) #Wraps command **bet** NodeHash_28c60a0 = pe.Node(interface=fsl.BET(), name='NodeName_28c60a0') #Generic datasink module to store structured outputs NodeHash_308ebc0 = pe.Node(interface=io.DataSink(), name='NodeName_308ebc0')