http://nipy.org/nipype/about.html#code-contributors
shell scripting:
Can be quick to do, and powerful, but only provides application specific scalability, and not easy to port across different architectures.
make/CMake:
Similar in concept to workflow execution in Nipype, but again limited by the need for command line tools and flexibility in terms of scaling across hardware architectures (although see makeflow.
Octave/MATLAB:
Integration with other tools is ad hoc (i.e., system call) and dataflow is managed at a programmatic level. However, see PSOM which offers a nice alternative to some aspects of Nipype for Octave/Matlab users.
Graphical options: (e.g., LONI Pipeline, VisTrails)
Are easy to use but reduces flexibility relative to scripting options.
Interface: Wraps a program or function
Node/MapNode: Wraps an Interface for use in a Workflow that provides
caching and other goodies (e.g., pseudo-sandbox)
Workflow: A graph or forest of graphs whose nodes are of type Node,
MapNode or Workflow and whose edges represent data flow
Plugin: A component that describes how a Workflow should be executed
Currently supported (5-2-2013). Click here for latest
Most used/contributed policy!
Not all components of these packages are available.
Available from @NeuroDebian, @PyPI, and @GitHub
- pip install nipype
- easy_install nipype
- sudo apt-get install python-nipypeDependencies: networkx, nibabel, numpy, scipy, traits
$ ipython notebook
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import nipype
# Comment the following section to increase verbosity of output
nipype.config.set('logging', 'workflow_level', 'CRITICAL')
nipype.config.set('logging', 'interface_level', 'CRITICAL')
nipype.logging.update_logging(nipype.config)
nipype.test(verbose=0) # Increase verbosity parameter for more info
If all goes well you will see an OK:
----------------------------------------------------------------------
Ran 2497 tests in 68.486s
OK (SKIP=13)
The number of tests and time will vary depending on which interfaces you have installed on your system.
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nipype.get_info()
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%pylab inline
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# Some preliminaries
import os
cwd = os.getcwd()
tutorial_dir = '/software/temp/nipype-tutorial/ohbm/'
if not os.path.exists(tutorial_dir):
os.mkdir(tutorial_dir)
os.chdir(tutorial_dir)
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import urllib
required_files = ['ds107/sub001/BOLD/task001_run001/bold.nii.gz',
'ds107/sub001/BOLD/task001_run002/bold.nii.gz',
'ds107/sub001/anatomy/highres001.nii.gz',
'ds107/sub044/BOLD/task001_run001/bold.nii.gz',
'ds107/sub044/BOLD/task001_run002/bold.nii.gz',
'ds107/sub044/anatomy/highres001.nii.gz'
]
base_url = 'http://openfmri.aws.amazon.com.s3.amazonaws.com/'
for filepath in required_files:
file_location = os.path.join(tutorial_dir, filepath)
if not os.path.exists(file_location):
print('Retrieving: ' + file_location)
os.makedirs(os.path.dirname(file_location))
urllib.urlretrieve(base_url + filepath, file_location)
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import nipype.algorithms
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from nipype.interfaces.fsl import DTIFit
from nipype.interfaces.spm import Realign
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DTIFit.help()
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Realign.help()
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import os
from shutil import copyfile
library_dir = os.path.join(tutorial_dir, 'as_a_library')
if not os.path.exists(library_dir):
os.mkdir(library_dir)
os.chdir(library_dir)
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from nipype.interfaces.freesurfer import MRIConvert
convert = MRIConvert(in_file='../ds107/sub001/BOLD/task001_run001/bold.nii.gz',
out_file='ds107.nii')
print(convert.cmdline)
results = convert.run(terminal_output='none') # allatonce, stream (default), file
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results.outputs
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convert = MRIConvert()
convert.inputs.in_file='../ds107/sub001/BOLD/task001_run001/bold.nii.gz'
convert.inputs.out_file='ds107.nii'
convert.run()
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convert = MRIConvert()
convert.run(in_file='../ds107/sub001/BOLD/task001_run001/bold.nii.gz',
out_file='ds107.nii')
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convert.inputs
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results.inputs
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from nipype.interfaces.spm import Realign
results1 = Realign(in_files='ds107.nii',
register_to_mean=False).run()
And now use FSL
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from nipype.interfaces.fsl import MCFLIRT
results2 = MCFLIRT(in_file='ds107.nii', ref_vol=0,
save_plots=True).run()
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print results1.runtime.duration, results2.runtime.duration
subplot(211);plot(genfromtxt('ds107_mcf.nii.gz.par')[:, 3:]);title('FSL')
subplot(212);plot(genfromtxt('rp_ds107.txt')[:,:3]);title('SPM')
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from nipype.caching import Memory
mem = Memory('.')
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spm_realign = mem.cache(Realign)
fsl_realign = mem.cache(MCFLIRT)
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spm_results = spm_realign(in_files='ds107.nii', register_to_mean=False)
fsl_results = fsl_realign(in_file='ds107.nii', ref_vol=0, save_plots=True)
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subplot(211);plot(genfromtxt(fsl_results.outputs.par_file)[:, 3:])
subplot(212);plot(genfromtxt(spm_results.outputs.realignment_parameters)[:,:3])
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spm_results = spm_realign(in_files='ds107.nii', register_to_mean=False)
fsl_results = fsl_realign(in_file='ds107.nii', ref_vol=0, save_plots=True)
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from os.path import abspath as opap
files = [opap('../ds107/sub001/BOLD/task001_run001/bold.nii.gz'),
opap('../ds107/sub001/BOLD/task001_run002/bold.nii.gz')]
converter = mem.cache(MRIConvert)
newfiles = []
for idx, fname in enumerate(files):
newfiles.append(converter(in_file=fname,
out_type='nii').outputs.out_file)
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os.chdir(tutorial_dir)
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from nipype.pipeline.engine import Node, MapNode, Workflow
Node:
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realign_spm = Node(Realign(), name='motion_correct')
Mapnode:
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convert2nii = MapNode(MRIConvert(out_type='nii'),
iterfield=['in_file'],
name='convert2nii')
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realignflow = Workflow(name='realign_with_spm')
realignflow.connect(convert2nii, 'out_file',
realign_spm, 'in_files')
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convert2nii.inputs.in_file = files
realign_spm.inputs.register_to_mean = False
realignflow.base_dir = opap('.')
realignflow.run()
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realignflow.write_graph()
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from IPython.core.display import Image
Image('realign_with_spm/graph.dot.png')
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realignflow.write_graph(graph2use='orig')
Image('realign_with_spm/graph_detailed.dot.png')
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os.chdir(tutorial_dir)
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from nipype.interfaces.io import DataGrabber, DataFinder
ds = Node(DataGrabber(infields=['subject_id'], outfields=['func']),
name='datasource')
ds.inputs.base_directory = opap('ds107')
ds.inputs.template = '%s/BOLD/task001*/bold.nii.gz'
ds.inputs.sort_filelist = True
ds.inputs.subject_id = 'sub001'
print ds.run().outputs
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ds.inputs.subject_id = 'sub044'
print ds.run().outputs
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ds = Node(DataGrabber(infields=['subject_id', 'task_id'],
outfields=['func', 'anat']),
name='datasource')
ds.inputs.base_directory = opap('ds107')
ds.inputs.template = '*'
ds.inputs.template_args = {'func': [['subject_id', 'task_id']],
'anat': [['subject_id']]}
ds.inputs.field_template = {'func': '%s/BOLD/task%03d*/bold.nii.gz',
'anat': '%s/anatomy/highres001.nii.gz'}
ds.inputs.sort_filelist = True
ds.inputs.subject_id = 'sub001'
ds.inputs.task_id = 1
print ds.run().outputs
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convert2nii = MapNode(MRIConvert(out_type='nii'),
iterfield=['in_file'],
name='convert2nii')
realign_spm = Node(Realign(), name='motion_correct')
realign_spm.inputs.register_to_mean = False
connectedworkflow = Workflow(name='connectedtogether')
connectedworkflow.base_dir = opap('working_dir')
connectedworkflow.connect(ds, 'func', convert2nii, 'in_file')
connectedworkflow.connect(convert2nii, 'out_file', realign_spm, 'in_files')
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from nipype.interfaces.io import DataSink
sinker = Node(DataSink(), name='sinker')
sinker.inputs.base_directory = opap('output')
connectedworkflow.connect(realign_spm, 'realigned_files',
sinker, 'realigned')
connectedworkflow.connect(realign_spm, 'realignment_parameters',
sinker, 'realigned.@parameters')
connectedworkflow.run()
'base_directory/container/parameterization/destloc/filename'
destloc = [@]string[[.[@]]string[[.[@]]string]...] and
destloc = realigned.@parameters --> 'realigned'
destloc = realigned.parameters.@1 --> 'realigned/parameters'
destloc = realigned.parameters.@2 --> 'realigned/parameters'
filename comes from the input to the connect statement.
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ds.iterables = ('subject_id', ['sub001', 'sub044'])
connectedworkflow.run()
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connectedworkflow.write_graph()
Image('working_dir/connectedtogether/graph.dot.png')
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from nipype.interfaces.utility import Function
def myfunc(input1, input2):
"""Add and subtract two inputs
"""
return input1 + input2, input1 - input2
calcfunc = Node(Function(input_names=['input1', 'input2'],
output_names = ['sum', 'difference'],
function=myfunc),
name='mycalc')
calcfunc.inputs.input1 = 1
calcfunc.inputs.input2 = 2
res = calcfunc.run()
print res.outputs
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connectedworkflow.run()
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connectedworkflow.run('MultiProc', plugin_args={'n_procs': 4})
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from os.path import abspath as opap
from nipype.interfaces.io import XNATSource
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl import BET
subject_id = 'xnat_S00001'
dg = Node(XNATSource(infields=['subject_id'],
outfields=['struct'],
config='/Users/satra/xnat_configs/nitrc_ir_config'),
name='xnatsource')
dg.inputs.query_template = ('/projects/fcon_1000/subjects/%s/experiments/xnat_E00001'
'/scans/%s/resources/NIfTI/files')
dg.inputs.query_template_args['struct'] = [['subject_id', 'anat_mprage_anonymized']]
dg.inputs.subject_id = subject_id
bet = Node(BET(), name='skull_stripper')
wf = Workflow(name='testxnat')
wf.base_dir = opap('xnattest')
wf.connect(dg, 'struct', bet, 'in_file')
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from nipype.interfaces.io import XNATSink
ds = Node(XNATSink(config='/Users/satra/xnat_configs/central_config'),
name='xnatsink')
ds.inputs.project_id = 'NPTEST'
ds.inputs.subject_id = 'NPTEST_xnat_S00001'
ds.inputs.experiment_id = 'test_xnat'
ds.inputs.reconstruction_id = 'bet'
ds.inputs.share = True
wf.connect(bet, 'out_file', ds, 'brain')
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wf.run()
Configurable options control workflow and node execution options
At the global level:
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from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
config.set('execution', 'stop_on_first_crash', 'true')
At the workflow level:
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wf.config['execution']['hash_method'] = 'content'
Configurations can also be set at the node level.
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bet.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
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wf.run()
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config.set_default_config()
logging.update_logging(config)
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from nipype.workflows.fmri.fsl.preprocess import create_susan_smooth
smooth = create_susan_smooth()
smooth.inputs.inputnode.in_files = opap('output/realigned/_subject_id_sub044/rbold_out.nii')
smooth.inputs.inputnode.fwhm = 5
smooth.inputs.inputnode.mask_file = 'mask.nii'
smooth.run() # Will error because mask.nii does not exist
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from nipype.interfaces.fsl import BET, MeanImage, ImageMaths
from nipype.pipeline.engine import Node
remove_nan = Node(ImageMaths(op_string= '-nan'), name='nanremove')
remove_nan.inputs.in_file = opap('output/realigned/_subject_id_sub044/rbold_out.nii')
mi = Node(MeanImage(), name='mean')
mask = Node(BET(mask=True), name='mask')
wf = Workflow('reuse')
wf.base_dir = opap('.')
wf.connect(remove_nan, 'out_file', mi, 'in_file')
wf.connect(mi, 'out_file', mask, 'in_file')
wf.connect(mask, 'out_file', smooth, 'inputnode.mask_file')
wf.connect(remove_nan, 'out_file', smooth, 'inputnode.in_files')
wf.run()
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print(smooth.list_node_names())
median = smooth.get_node('median')
median.inputs.op_string = '-k %s -p 60'
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wf.run()
This tutorial covers the concepts of Nipype
It will allow you to reuse and debug the various workflows available in Nipype, BIPS and CPAC
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import os
basedir = '/Users/satra/Dropbox/WORK/notebooks/'
if os.path.exists(basedir):
os.chdir(basedir)