def make_nipype_pipeline(self, base_dir):
from nipype import config
config.enable_provenance()
import nipype.pipeline.engine as pe
import re
self.compute_dependencies()
nodes = []
pipeline = pe.Workflow('converted_to_nipype', base_dir)
i=0
for command in self.commands:
(interface, input_map, output_map) = make_nipype_function_interface(command)
comment = re.sub(r'\W','_', command.comment)
#comment = command.comment.replace(' ','_')
nodes.append((pe.Node(interface=interface, name=comment),
input_map, output_map))
i+=1
(input_nodes, mapping) = self.make_selectfiles(nodes)
for i in xrange(len(self.commands)):
(parent_node, parent_inputs, parent_outputs) = nodes[i]
for (input_file, input_name) in parent_inputs.items():
if input_file in mapping:
(fields, j) = mapping[input_file]
input_source = input_nodes[j]
nipype_input_name = make_nipype_name(fields)
pipeline.connect([(input_source, parent_node, [(nipype_input_name, input_name)])])
for j in xrange(i, len(self.commands)):
(child_node, child_inputs, _) = nodes[j]
files = self.dependency_files[i,j]
for file in files:
assert file in parent_outputs
assert file in child_inputs
pipeline.connect([(parent_node, child_node, [(parent_outputs[file], child_inputs[file])])])
return pipeline
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(op.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
return eg, prov_file
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
dicom_dir = op.dirname(item_dicoms[0]) if item_dicoms else None
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_dir = dicom_dir
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def nipype_convert(item_dicoms,
prefix,
with_prov,
bids_options,
tmpdir,
dcmconfig=None):
"""
Converts DICOMs grouped from heuristic using Nipype's Dcm2niix interface.
Parameters
----------
item_dicoms : List
DICOM files to convert
prefix : String
Heuristic output path
with_prov : Bool
Store provenance information
bids_options : List or None
If not None then output BIDS sidecar JSONs
List may contain bids specific options
tmpdir : Directory
Conversion working directory
dcmconfig : File (optional)
JSON file used for additional Dcm2niix configuration
"""
import nipype
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
fromfile = dcmconfig if dcmconfig else None
if fromfile:
lgr.info("Using custom config file %s", fromfile)
convertnode = Node(Dcm2niix(from_file=fromfile), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = prefix
if nipype.__version__.split('.')[0] == '0':
# deprecated since 1.0, might be needed(?) before
convertnode.inputs.terminal_output = 'allatonce'
else:
convertnode.terminal_output = 'allatonce'
convertnode.inputs.bids_format = bids_options is not None
eg = convertnode.run()
# prov information
prov_file = prefix + '_prov.ttl' if with_prov else None
if prov_file:
safe_copyfile(
op.join(convertnode.base_dir, convertnode.name, 'provenance.ttl'),
prov_file)
return eg, prov_file
def test_provenance_exists(tmpdir):
tmpdir.chdir()
from nipype import config
from nipype.interfaces.base import CommandLine
provenance_state = config.get('execution', 'write_provenance')
hash_state = config.get('execution', 'hash_method')
config.enable_provenance()
CommandLine('echo hello').run()
config.set('execution', 'write_provenance', provenance_state)
config.set('execution', 'hash_method', hash_state)
assert tmpdir.join('provenance.provn').check()
def test_provenance_exists(tmpdir):
tmpdir.chdir()
from nipype import config
from nipype.interfaces.base import CommandLine
provenance_state = config.get('execution', 'write_provenance')
hash_state = config.get('execution', 'hash_method')
config.enable_provenance()
CommandLine('echo hello').run()
config.set('execution', 'write_provenance', provenance_state)
config.set('execution', 'hash_method', hash_state)
assert tmpdir.join('provenance.provn').check()
def test_provenance_exists(tmpdir):
tmpdir.chdir()
from nipype import config
from nipype.interfaces.base import CommandLine
provenance_state = config.get("execution", "write_provenance")
hash_state = config.get("execution", "hash_method")
config.enable_provenance()
CommandLine("echo hello").run()
config.set("execution", "write_provenance", provenance_state)
config.set("execution", "hash_method", hash_state)
assert tmpdir.join("provenance.provn").check()
def test_provenance_exists(tmpdir):
tempdir = str(tmpdir)
os.chdir(tempdir)
from nipype import config
from nipype.interfaces.base import CommandLine
provenance_state = config.get('execution', 'write_provenance')
hash_state = config.get('execution', 'hash_method')
config.enable_provenance()
CommandLine('echo hello').run()
config.set('execution', 'write_provenance', provenance_state)
config.set('execution', 'hash_method', hash_state)
provenance_exists = os.path.exists(os.path.join(tempdir, 'provenance.provn'))
assert provenance_exists
def test_provenance_exists(tmpdir):
tempdir = str(tmpdir)
os.chdir(tempdir)
from nipype import config
from nipype.interfaces.base import CommandLine
provenance_state = config.get('execution', 'write_provenance')
hash_state = config.get('execution', 'hash_method')
config.enable_provenance()
CommandLine('echo hello').run()
config.set('execution', 'write_provenance', provenance_state)
config.set('execution', 'hash_method', hash_state)
provenance_exists = os.path.exists(
os.path.join(tempdir, 'provenance.provn'))
assert provenance_exists
def run_examples(example, pipelines, data_path, plugin=None, rm_base_dir=True):
from nipype import config
from nipype.interfaces.base import CommandLine
if plugin is None:
plugin = 'MultiProc'
print('running example: %s with plugin: %s' % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == 'MultiProc':
plugin_args['n_procs'] = int(
os.getenv('NIPYPE_NUMBER_OF_CPUS', cpu_count()))
module = import_module('.' + example, 'niflow.nipype1.examples')
for pipeline in pipelines:
wf = getattr(module, pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
results_dir = os.path.join(wf.base_dir, wf.name)
if rm_base_dir and os.path.exists(results_dir):
rmtree(results_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), 'logs', example)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
wf.config = {
'execution': {
'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true',
'poll_sleep_duration': 2
},
'logging': {
'log_directory': log_dir,
'log_to_file': True
}
}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def run_examples(example, pipelines, data_path, plugin=None, rm_base_dir=True):
from nipype import config
from nipype.interfaces.base import CommandLine
if plugin is None:
plugin = 'MultiProc'
print('running example: %s with plugin: %s' % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == 'MultiProc':
plugin_args['n_procs'] = int(
os.getenv('NIPYPE_NUMBER_OF_CPUS', cpu_count()))
__import__(example)
for pipeline in pipelines:
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
results_dir = os.path.join(wf.base_dir, wf.name)
if rm_base_dir and os.path.exists(results_dir):
rmtree(results_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), 'logs', example)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
wf.config = {
'execution': {
'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true',
'poll_sleep_duration': 2
},
'logging': {
'log_directory': log_dir,
'log_to_file': True
}
}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def nipype_convert(item_dicoms, prefix, with_prov, bids, tmpdir):
""" """
if with_prov:
from nipype import config
config.enable_provenance()
from nipype import Node
from nipype.interfaces.dcm2nii import Dcm2niix
item_dicoms = list(map(op.abspath, item_dicoms)) # absolute paths
convertnode = Node(Dcm2niix(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item_dicoms
convertnode.inputs.out_filename = op.basename(op.dirname(prefix))
convertnode.inputs.bids_format = bids
convertnode.inputs.anon_bids = False
return convertnode.run()
def run_examples(example, pipelines, data_path, plugin=None, rm_base_dir=True):
from nipype import config
from nipype.interfaces.base import CommandLine
if plugin is None:
plugin = "MultiProc"
print("running example: %s with plugin: %s" % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == "MultiProc":
plugin_args["n_procs"] = int(os.getenv("NIPYPE_NUMBER_OF_CPUS", cpu_count()))
__import__(example)
for pipeline in pipelines:
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), "output", example, plugin)
results_dir = os.path.join(wf.base_dir, wf.name)
if rm_base_dir and os.path.exists(results_dir):
rmtree(results_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), "logs", example)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
wf.config = {
"execution": {
"hash_method": "timestamp",
"stop_on_first_rerun": "true",
"write_provenance": "true",
"poll_sleep_duration": 2,
},
"logging": {"log_directory": log_dir, "log_to_file": True},
}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def run_examples(example, pipelines, plugin):
print('running example: %s with plugin: %s' % (example, plugin))
from nipype import config
config.enable_debug_mode()
config.enable_provenance()
from nipype.interfaces.base import CommandLine
CommandLine.set_default_terminal_output("stream")
__import__(example)
for pipeline in pipelines:
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
if os.path.exists(wf.base_dir):
rmtree(wf.base_dir)
wf.config = {'execution': {'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true'}}
wf.run(plugin=plugin, plugin_args={'n_procs': 4})
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def run_examples(example, pipelines, data_path, plugin=None):
from nipype import config
from nipype.interfaces.base import CommandLine
if plugin is None:
plugin = 'MultiProc'
print('running example: %s with plugin: %s' % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == 'MultiProc':
plugin_args['n_procs'] = cpu_count()
__import__(example)
for pipeline in pipelines:
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
if os.path.exists(wf.base_dir):
rmtree(wf.base_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), 'logs', example)
if os.path.exists(log_dir):
rmtree(log_dir)
os.makedirs(log_dir)
wf.config = {'execution': {'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true'},
'logging': {'log_directory': log_dir, 'log_to_file': True}}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def run_examples(example, pipelines, plugin):
print('running example: %s with plugin: %s' % (example, plugin))
from nipype import config
config.enable_debug_mode()
config.enable_provenance()
from nipype.interfaces.base import CommandLine
CommandLine.set_default_terminal_output("stream")
__import__(example)
for pipeline in pipelines:
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
if os.path.exists(wf.base_dir):
rmtree(wf.base_dir)
wf.config = {
'execution': {
'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true'
}
}
wf.run(plugin=plugin, plugin_args={'n_procs': 4})
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
============================
fMRI: OpenfMRI.org data, FSL
============================
A growing number of datasets are available on `OpenfMRI <http://openfmri.org>`_.
This script demonstrates how to use nipype to analyze a data set.
python fmri_openfmri.py --datasetdir ds107
"""
from nipype import config
config.enable_provenance()
from nipype.external import six
from glob import glob
import os
import nipype.pipeline.engine as pe
import nipype.algorithms.modelgen as model
import nipype.algorithms.rapidart as ra
import nipype.interfaces.fsl as fsl
import nipype.interfaces.io as nio
import nipype.interfaces.utility as niu
from nipype.workflows.fmri.fsl import (create_featreg_preproc,
create_modelfit_workflow,
create_fixed_effects_flow,
create_reg_workflow)
def convert(items, anonymizer=None, symlink=True, converter=None):
prov_files = []
tmpdir = mkdtemp()
for item in items:
if isinstance(item[1], (list, tuple)):
outtypes = item[1]
else:
outtypes = [item[1]]
prefix = item[0]
print('Converting %s' % prefix)
dirname = os.path.dirname(prefix + '.ext')
print(dirname)
if not os.path.exists(dirname):
os.makedirs(dirname)
for outtype in outtypes:
print(outtype)
if outtype == 'dicom':
dicomdir = prefix + '_dicom'
if os.path.exists(dicomdir):
shutil.rmtree(dicomdir)
os.mkdir(dicomdir)
for filename in item[2]:
outfile = os.path.join(dicomdir, os.path.split(filename)[1])
if not os.path.islink(outfile):
if symlink:
os.symlink(filename, outfile)
else:
os.link(filename, outfile)
elif outtype in ['nii', 'nii.gz']:
outname = prefix + '.' + outtype
scaninfo = prefix + '_scaninfo.json'
if not os.path.exists(outname):
from nipype import config
config.enable_provenance()
from nipype import Function, Node
from nipype.interfaces.base import isdefined
print converter
if converter == 'mri_convert':
from nipype.interfaces.freesurfer.preprocess import MRIConvert
convertnode = Node(MRIConvert(), name = 'convert')
convertnode.base_dir = tmpdir
if outtype == 'nii.gz':
convertnode.inputs.out_type = 'niigz'
convertnode.inputs.in_file = item[2][0]
convertnode.inputs.out_file = outname
#cmd = 'mri_convert %s %s' % (item[2][0], outname)
#print(cmd)
#os.system(cmd)
res=convertnode.run()
elif converter == 'dcm2nii':
from nipype.interfaces.dcm2nii import Dcm2nii
convertnode = Node(Dcm2nii(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item[2]
convertnode.inputs.gzip_output = outtype == 'nii.gz'
convertnode.inputs.terminal_output = 'allatonce'
res = convertnode.run()
if isinstance(res.outputs.converted_files, list):
print("Cannot convert dicom files - series likely has multiple orientations: ", item[2])
continue
else:
shutil.copyfile(res.outputs.converted_files, outname)
if isdefined(res.outputs.bvecs):
outname_bvecs = prefix + '.bvecs'
outname_bvals = prefix + '.bvals'
shutil.copyfile(res.outputs.bvecs, outname_bvecs)
shutil.copyfile(res.outputs.bvals, outname_bvals)
prov_file = prefix + '_prov.ttl'
shutil.copyfile(os.path.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
prov_files.append(prov_file)
embedfunc = Node(Function(input_names=['dcmfiles',
'niftifile',
'infofile',
'force'],
output_names=['outfile',
'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item[2]
embedfunc.inputs.niftifile = outname
embedfunc.inputs.infofile = scaninfo
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
res = embedfunc.run()
g = res.provenance.rdf()
g.parse(prov_file,
format='turtle')
g.serialize(prov_file, format='turtle')
#out_file, meta_dict = embed_nifti(item[2], outname, force=True)
os.chmod(outname, 0440)
os.chmod(scaninfo, 0440)
os.chmod(prov_file, 0440)
shutil.rmtree(tmpdir)
def run_examples(example, pipelines, data_path, plugin=None):
'''
Run example workflows
'''
# Import packages
from nipype import config
from nipype.interfaces.base import CommandLine
from nipype.utils import draw_gantt_chart
from nipype.pipeline.plugins import log_nodes_cb
if plugin is None:
plugin = 'MultiProc'
print('running example: %s with plugin: %s' % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == 'MultiProc':
plugin_args['n_procs'] = cpu_count()
__import__(example)
for pipeline in pipelines:
# Init and run workflow
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
if os.path.exists(wf.base_dir):
rmtree(wf.base_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), 'logs', example)
if os.path.exists(log_dir):
rmtree(log_dir)
os.makedirs(log_dir)
wf.config = {
'execution': {
'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true'
}
}
# Callback log setup
if example == 'fmri_spm_nested' and plugin == 'MultiProc' and \
pipeline == 'l2pipeline':
# Init callback log
import logging
cb_log_path = os.path.join(os.path.expanduser('~'), 'callback.log')
cb_logger = logging.getLogger('callback')
cb_logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(cb_log_path)
cb_logger.addHandler(handler)
plugin_args = {'n_procs': 4, 'status_callback': log_nodes_cb}
else:
plugin_args = {'n_procs': 4}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# Draw gantt chart only if pandas is installed
try:
import pandas
pandas_flg = True
except ImportError as exc:
pandas_flg = False
if plugin_args.has_key('status_callback') and pandas_flg:
draw_gantt_chart.generate_gantt_chart(cb_log_path, 4)
dst_log_html = os.path.join(os.path.expanduser('~'),
'callback.log.html')
copyfile(cb_log_path + '.html', dst_log_html)
def convert(items, anonymizer=None, symlink=True, converter=None):
prov_files = []
tmpdir = mkdtemp()
for item in items:
if isinstance(item[1], (list, tuple)):
outtypes = item[1]
else:
outtypes = [item[1]]
prefix = item[0]
print('Converting %s' % prefix)
dirname = os.path.dirname(prefix + '.ext')
print(dirname)
if not os.path.exists(dirname):
os.makedirs(dirname)
for outtype in outtypes:
print(outtype)
if outtype == 'dicom':
dicomdir = prefix + '_dicom'
if os.path.exists(dicomdir):
shutil.rmtree(dicomdir)
os.mkdir(dicomdir)
for filename in item[2]:
outfile = os.path.join(dicomdir, os.path.split(filename)[1])
if not os.path.islink(outfile):
if symlink:
os.symlink(filename, outfile)
else:
os.link(filename, outfile)
elif outtype in ['nii', 'nii.gz']:
outname = prefix + '.' + outtype
scaninfo = prefix + '_scaninfo.json'
if not os.path.exists(outname):
from nipype import config
config.enable_provenance()
from nipype import Function, Node
from nipype.interfaces.base import isdefined
print converter
if converter == 'mri_convert':
from nipype.interfaces.freesurfer.preprocess import MRIConvert
convertnode = Node(MRIConvert(), name = 'convert')
convertnode.base_dir = tmpdir
if outtype == 'nii.gz':
convertnode.inputs.out_type = 'niigz'
convertnode.inputs.in_file = item[2][0]
convertnode.inputs.out_file = outname
#cmd = 'mri_convert %s %s' % (item[2][0], outname)
#print(cmd)
#os.system(cmd)
res=convertnode.run()
elif converter == 'dcm2nii':
from nipype.interfaces.dcm2nii import Dcm2nii
convertnode = Node(Dcm2nii(), name='convert')
convertnode.base_dir = tmpdir
convertnode.inputs.source_names = item[2]
convertnode.inputs.gzip_output = outtype == 'nii.gz'
convertnode.inputs.terminal_output = 'allatonce'
res = convertnode.run()
if isinstance(res.outputs.converted_files, list):
print("Cannot convert dicom files - series likely has multiple orientations: ", item[2])
continue
else:
shutil.copyfile(res.outputs.converted_files, outname)
if isdefined(res.outputs.bvecs):
outname_bvecs = prefix + '.bvecs'
outname_bvals = prefix + '.bvals'
shutil.copyfile(res.outputs.bvecs, outname_bvecs)
shutil.copyfile(res.outputs.bvals, outname_bvals)
prov_file = prefix + '_prov.ttl'
shutil.copyfile(os.path.join(convertnode.base_dir,
convertnode.name,
'provenance.ttl'),
prov_file)
prov_files.append(prov_file)
embedfunc = Node(Function(input_names=['dcmfiles',
'niftifile',
'infofile',
'force'],
output_names=['outfile',
'meta'],
function=embed_nifti),
name='embedder')
embedfunc.inputs.dcmfiles = item[2]
embedfunc.inputs.niftifile = outname
embedfunc.inputs.infofile = scaninfo
embedfunc.inputs.force = True
embedfunc.base_dir = tmpdir
res = embedfunc.run()
g = res.provenance.rdf()
g.parse(prov_file,
format='turtle')
g.serialize(prov_file, format='turtle')
#out_file, meta_dict = embed_nifti(item[2], outname, force=True)
os.chmod(outname, 0440)
os.chmod(scaninfo, 0440)
os.chmod(prov_file, 0440)
shutil.rmtree(tmpdir)
#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
=============================================
fMRI: OpenfMRI.org data, FSL, ANTS, c3daffine
=============================================
A growing number of datasets are available on `OpenfMRI <http://openfmri.org>`_.
This script demonstrates how to use nipype to analyze a data set::
python fmri_ants_openfmri.py --datasetdir ds107
"""
from nipype import config
config.enable_provenance()
from nipype.external import six
from glob import glob
import os
import nipype.pipeline.engine as pe
import nipype.algorithms.modelgen as model
import nipype.algorithms.rapidart as ra
import nipype.interfaces.fsl as fsl
import nipype.interfaces.ants as ants
from nipype.algorithms.misc import TSNR
from nipype.interfaces.c3 import C3dAffineTool
import nipype.interfaces.io as nio
import nipype.interfaces.utility as niu
def run_examples(example, pipelines, data_path, plugin=None):
'''
Run example workflows
'''
# Import packages
from nipype import config
from nipype.interfaces.base import CommandLine
from nipype.utils import draw_gantt_chart
from nipype.pipeline.plugins import log_nodes_cb
if plugin is None:
plugin = 'MultiProc'
print('running example: %s with plugin: %s' % (example, plugin))
config.enable_debug_mode()
config.enable_provenance()
CommandLine.set_default_terminal_output("stream")
plugin_args = {}
if plugin == 'MultiProc':
plugin_args['n_procs'] = cpu_count()
__import__(example)
for pipeline in pipelines:
# Init and run workflow
wf = getattr(sys.modules[example], pipeline)
wf.base_dir = os.path.join(os.getcwd(), 'output', example, plugin)
if os.path.exists(wf.base_dir):
rmtree(wf.base_dir)
# Handle a logging directory
log_dir = os.path.join(os.getcwd(), 'logs', example)
if os.path.exists(log_dir):
rmtree(log_dir)
os.makedirs(log_dir)
wf.config = {'execution': {'hash_method': 'timestamp',
'stop_on_first_rerun': 'true',
'write_provenance': 'true'}}
# Callback log setup
if example == 'fmri_spm_nested' and plugin == 'MultiProc' and \
pipeline == 'l2pipeline':
# Init callback log
import logging
cb_log_path = os.path.join(os.path.expanduser('~'), 'callback.log')
cb_logger = logging.getLogger('callback')
cb_logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(cb_log_path)
cb_logger.addHandler(handler)
plugin_args = {'n_procs' : 4, 'status_callback' : log_nodes_cb}
else:
plugin_args = {'n_procs' : 4}
try:
wf.inputs.inputnode.in_data = os.path.abspath(data_path)
except AttributeError:
pass # the workflow does not have inputnode.in_data
wf.run(plugin=plugin, plugin_args=plugin_args)
# Draw gantt chart only if pandas is installed
try:
import pandas
pandas_flg = True
except ImportError as exc:
pandas_flg = False
if plugin_args.has_key('status_callback') and pandas_flg:
draw_gantt_chart.generate_gantt_chart(cb_log_path, 4)
dst_log_html = os.path.join(os.path.expanduser('~'), 'callback.log.html')
copyfile(cb_log_path+'.html', dst_log_html)
