def test_Commandline_environ(monkeypatch, tmpdir):
from nipype import config
config.set_default_config()
tmpdir.chdir()
monkeypatch.setitem(os.environ, 'DISPLAY', ':1')
# Test environment
ci3 = nib.CommandLine(command='echo')
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':1'
# Test display_variable option
monkeypatch.delitem(os.environ, 'DISPLAY', raising=False)
config.set('execution', 'display_variable', ':3')
res = ci3.run()
assert 'DISPLAY' not in ci3.inputs.environ
assert 'DISPLAY' not in res.runtime.environ
# If the interface has _redirect_x then yes, it should be set
ci3._redirect_x = True
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':3'
# Test overwrite
monkeypatch.setitem(os.environ, 'DISPLAY', ':1')
ci3.inputs.environ = {'DISPLAY': ':2'}
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':2'
def main():
config.set('logging', 'workflow_level', 'CRITICAL')
# Find the functions that create workflows
wf_funcs = [k for k in dir(wf) if re.match("create_.*_workflow", k)]
for func in wf_funcs:
try:
out = getattr(wf, func)()
except:
print "ERROR: call to %s failed" % func
# Some of the workflow functions return (flow, inputs, outputs)
try:
flow, _, _ = out
except TypeError:
flow = out
# Write the graphs
name = flow.name
flow.write_graph("graphs/%s.dot" % name, "orig")
# Remove the .dot files as they are not of use to us
files = glob("graphs/*")
for f in files:
if f.endswith(".dot"):
os.remove(f)
def test_Commandline_environ(monkeypatch, tmpdir):
from nipype import config
config.set_default_config()
tmpdir.chdir()
monkeypatch.setitem(os.environ, "DISPLAY", ":1")
# Test environment
ci3 = nib.CommandLine(command="echo")
res = ci3.run()
assert res.runtime.environ["DISPLAY"] == ":1"
# Test display_variable option
monkeypatch.delitem(os.environ, "DISPLAY", raising=False)
config.set("execution", "display_variable", ":3")
res = ci3.run()
assert "DISPLAY" not in ci3.inputs.environ
assert "DISPLAY" not in res.runtime.environ
# If the interface has _redirect_x then yes, it should be set
ci3._redirect_x = True
res = ci3.run()
assert res.runtime.environ["DISPLAY"] == ":3"
# Test overwrite
monkeypatch.setitem(os.environ, "DISPLAY", ":1")
ci3.inputs.environ = {"DISPLAY": ":2"}
res = ci3.run()
assert res.runtime.environ["DISPLAY"] == ":2"
def test_Commandline_environ(monkeypatch, tmpdir):
from nipype import config
config.set_default_config()
tmpdir.chdir()
monkeypatch.setitem(os.environ, 'DISPLAY', ':1')
# Test environment
ci3 = nib.CommandLine(command='echo')
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':1'
# Test display_variable option
monkeypatch.delitem(os.environ, 'DISPLAY', raising=False)
config.set('execution', 'display_variable', ':3')
res = ci3.run()
assert 'DISPLAY' not in ci3.inputs.environ
assert 'DISPLAY' not in res.runtime.environ
# If the interface has _redirect_x then yes, it should be set
ci3._redirect_x = True
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':3'
# Test overwrite
monkeypatch.setitem(os.environ, 'DISPLAY', ':1')
ci3.inputs.environ = {'DISPLAY': ':2'}
res = ci3.run()
assert res.runtime.environ['DISPLAY'] == ':2'
def init_logging(workdir):
"""
Add new logging handler to nipype to output to log directory
:param workdir: Log directory
"""
fp = os.path.join(workdir, "pipeline.json")
with open(fp, "r") as f:
data = json.load(f)
images = transpose(data["images"])
real_output_dir = os.path.join(workdir, "log")
hdlr = WfHandler(real_output_dir, images)
from nipype import logging as nlogging
from nipype import config
formatter = Formatter(fmt=nlogging.fmt, datefmt=nlogging.datefmt)
hdlr.setFormatter(formatter)
config.set("logging", "interface_level", "DEBUG")
nlogging.update_logging(config)
nlogging._iflogger.handlers = []
nlogging._iflogger.propagate = False
nlogging._iflogger.addHandler(hdlr)
nlogging._logger.handlers = []
nlogging._logger.propagate = True
nlogging._logger.addHandler(hdlr)
def main(arglist):
config.set('logging', 'workflow_level', 'CRITICAL')
# Find the functions that create workflows
wf_funcs = [k for k in dir(wf) if re.match("create_.*_workflow", k)]
for func in wf_funcs:
try:
out = getattr(wf, func)()
except:
print("ERROR: call to %s failed" % func)
# Some of the workflow functions return (flow, inputs, outputs)
try:
flow, _, _ = out
except TypeError:
flow = out
# Write the graphs
name = flow.name
if arglist:
if name in arglist:
flow.write_graph("graphs/%s.dot" % name, "orig", format="svg")
else:
flow.write_graph("graphs/%s.dot" % name, "orig", format="svg")
# Remove the .dot files as they are not of use to us
files = glob("graphs/*")
for f in files:
if f.endswith(".dot"):
os.remove(f)
def run(args):
"""Get and process specific information"""
project = gather_project_info()
exp = gather_experiment_info(args.experiment, args.model)
# Subject is always highest level of parameterization
subject_list = determine_subjects(args.subjects)
subj_source = make_subject_source(subject_list)
# Get the full correct name for the experiment
if args.experiment is None:
exp_name = project["default_exp"]
else:
exp_name = args.experiment
exp['exp_name'] = exp_name
exp['model_name'] = args.model if args.model else ''
# Set roots of output storage
project['analysis_dir'] = op.join(project["analysis_dir"], exp_name)
project['working_dir'] = op.join(project["working_dir"], exp_name,
exp['model_name'])
config.set("execution", "crashdump_dir", project["crash_dir"])
if args.verbose > 0:
config.set("logging", "filemanip_level", 'DEBUG')
config.enable_debug_mode()
logging.update_logging(config)
if not op.exists(project['analysis_dir']):
os.makedirs(project['analysis_dir'])
workflows_dir = os.path.join(os.environ['FITZ_DIR'], exp['pipeline'],
'workflows')
if not op.isdir(workflows_dir):
missing_pipe = 'raise'
if missing_pipe == 'install':
install(args)
else:
raise IOError("Run `fitz install` to set up your pipeline of "
"workflows, %s does not exist." % workflows_dir)
sys.path.insert(0, workflows_dir)
for wf_name in args.workflows:
try:
mod = imp.find_module(wf_name)
wf_module = imp.load_module("wf", *mod)
except (IOError, ImportError):
print "Could not find any workflows matching %s" % wf_name
raise
params = update_params(wf_module, exp)
workflow = wf_module.workflow_manager(project, params, args,
subj_source)
# Run the pipeline
plugin, plugin_args = determine_engine(args)
workflow.write_graph(str(workflow) + '.dot', format='svg')
if not args.dontrun:
workflow.run(plugin, plugin_args)
def run(args):
"""Get and process specific information"""
project = gather_project_info()
exp = gather_experiment_info(args.experiment, args.model)
# Subject is always highest level of parameterization
subject_list = determine_subjects(args.subjects)
subj_source = make_subject_source(subject_list)
# Get the full correct name for the experiment
if args.experiment is None:
exp_name = project["default_exp"]
else:
exp_name = args.experiment
exp['exp_name'] = exp_name
exp['model_name'] = args.model if args.model else ''
# Set roots of output storage
project['analysis_dir'] = op.join(project["analysis_dir"], exp_name)
project['working_dir'] = op.join(project["working_dir"], exp_name,
exp['model_name'])
config.set("execution", "crashdump_dir", project["crash_dir"])
if args.verbose > 0:
config.set("logging", "filemanip_level", 'DEBUG')
config.enable_debug_mode()
logging.update_logging(config)
if not op.exists(project['analysis_dir']):
os.makedirs(project['analysis_dir'])
workflows_dir = os.path.join(os.environ['FITZ_DIR'], exp['pipeline'],
'workflows')
if not op.isdir(workflows_dir):
missing_pipe = 'raise'
if missing_pipe == 'install':
install(args)
else:
raise IOError("Run `fitz install` to set up your pipeline of "
"workflows, %s does not exist." % workflows_dir)
sys.path.insert(0, workflows_dir)
for wf_name in args.workflows:
try:
mod = imp.find_module(wf_name)
wf_module = imp.load_module("wf", *mod)
except (IOError, ImportError):
print "Could not find any workflows matching %s" % wf_name
raise
params = update_params(wf_module, exp)
workflow = wf_module.workflow_manager(
project, params, args, subj_source)
# Run the pipeline
plugin, plugin_args = determine_engine(args)
workflow.write_graph(str(workflow)+'.dot', format='svg')
if not args.dontrun:
workflow.run(plugin, plugin_args)
def test_display_config(monkeypatch, dispnum):
"""Check that the display_variable option is used ($DISPLAY not set)"""
config._display = None
dispstr = ':%d' % dispnum
config.set('execution', 'display_variable', dispstr)
monkeypatch.delitem(os.environ, 'DISPLAY', raising=False)
assert config.get_display() == config.get('execution', 'display_variable')
# Test that it was correctly cached
assert config.get_display() == config.get('execution', 'display_variable')
def test_display_config_and_system(monkeypatch):
"""Check that when only both config and $DISPLAY are defined, the config takes precedence"""
config._display = None
dispstr = ':10'
config.set('execution', 'display_variable', dispstr)
monkeypatch.setitem(os.environ, 'DISPLAY', ':0')
assert config.get_display() == dispstr
# Test that it was correctly cached
assert config.get_display() == dispstr
def test_display_config_and_system(monkeypatch):
"""Check that when only both config and $DISPLAY are defined, the config
takes precedence"""
config._display = None
dispstr = ":10"
config.set("execution", "display_variable", dispstr)
monkeypatch.setenv("DISPLAY", ":0")
assert config.get_display() == dispstr
# Test that it was correctly cached
assert config.get_display() == dispstr
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_cmdline_profiling(tmpdir, mem_gb, n_procs):
"""
Test runtime profiler correctly records workflow RAM/CPUs consumption
of a CommandLine-derived interface
"""
from nipype import config
config.set('execution', 'resource_monitor_frequency', '0.2') # Force sampling fast
tmpdir.chdir()
iface = UseResources(mem_gb=mem_gb, n_procs=n_procs)
result = iface.run()
assert abs(mem_gb - result.runtime.mem_peak_gb) < 0.3, 'estimated memory error above .3GB'
assert int(result.runtime.cpu_percent / 100 + 0.2) == n_procs, 'wrong number of threads estimated'
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_cmdline_profiling(tmpdir, mem_gb, n_procs):
"""
Test runtime profiler correctly records workflow RAM/CPUs consumption
of a CommandLine-derived interface
"""
from nipype import config
config.set('monitoring', 'sample_frequency', '0.2') # Force sampling fast
tmpdir.chdir()
iface = UseResources(mem_gb=mem_gb, n_procs=n_procs)
result = iface.run()
assert abs(mem_gb - result.runtime.mem_peak_gb) < 0.3, 'estimated memory error above .3GB'
assert int(result.runtime.cpu_percent / 100 + 0.2) == n_procs, 'wrong number of threads estimated'
def test_function_profiling(tmpdir, mem_gb, n_procs):
"""
Test runtime profiler correctly records workflow RAM/CPUs consumption
of a Function interface
"""
from nipype import config
config.set('execution', 'resource_monitor_frequency', '0.2') # Force sampling fast
tmpdir.chdir()
iface = niu.Function(function=_use_resources)
iface.inputs.mem_gb = mem_gb
iface.inputs.n_procs = n_procs
result = iface.run()
assert abs(mem_gb - result.runtime.mem_peak_gb) < 0.3, 'estimated memory error above .3GB'
assert int(result.runtime.cpu_percent / 100 + 0.2) >= n_procs
def test_function_profiling(tmpdir, mem_gb, n_procs):
"""
Test runtime profiler correctly records workflow RAM/CPUs consumption
of a Function interface
"""
from nipype import config
config.set('monitoring', 'sample_frequency', '0.2') # Force sampling fast
tmpdir.chdir()
iface = niu.Function(function=_use_resources)
iface.inputs.mem_gb = mem_gb
iface.inputs.n_procs = n_procs
result = iface.run()
assert abs(mem_gb - result.runtime.mem_peak_gb) < 0.3, 'estimated memory error above .3GB'
assert int(result.runtime.cpu_percent / 100 + 0.2) >= n_procs
def convert_tck_to_trk(filename_in, filename_out, reference_affine, compress_err_thr=0.1, smooth=None):
'''
Convert tck file to trk file and compress
:param filename_in:
:param filename_out:
:param compress_err_thr: compress fibers if setting error threshold here (default: 0.1mm)
:param smooth: smooth streamlines (default: None)
10: slight smoothing, 100: very smooth from beginning to end
:return:
'''
#Hide large number of nipype logging outputs
from nipype import config, logging
config.set('execution', 'remove_unnecessary_outputs', 'true')
config.set('logging', 'workflow_level', 'WARNING')
config.set('logging', 'interface_level', 'WARNING')
logging.update_logging(config)
from nipype.interfaces.mrtrix.convert import read_mrtrix_tracks
from dipy.tracking.metrics import spline
hdr, streamlines = read_mrtrix_tracks(filename_in, as_generator=False) # Load Fibers (Tck)
if smooth is not None:
streamlines_smooth = []
for sl in streamlines:
streamlines_smooth.append(spline(sl, s=smooth))
streamlines = streamlines_smooth
#Compressing also good to remove checkerboard artefacts from tracking on peaks
if compress_err_thr is not None:
streamlines = FiberUtils.compress_streamlines(streamlines, compress_err_thr)
FiberUtils.save_streamlines_as_trk(filename_out, streamlines, reference_affine)
def convert_tck_to_trk(filename_in,
filename_out,
reference_affine,
compress_err_thr=0.1):
'''
Convert tck file to trk file and compress
:param filename_in:
:param filename_out:
:param compress_err_thr: compress fibers if setting error threshold here (default: 0.1mm)
:return:
'''
#Hide large number of nipype logging outputs
from nipype import config, logging
config.set('execution', 'remove_unnecessary_outputs', 'true')
config.set('logging', 'workflow_level', 'WARNING')
config.set('logging', 'interface_level', 'WARNING')
logging.update_logging(config)
from nipype.interfaces.mrtrix.convert import read_mrtrix_tracks
hdr, streamlines = read_mrtrix_tracks(
filename_in, as_generator=False) # Load Fibers (Tck)
#Compressing also good to remove checkerboard artefacts from tracking on peaks
if compress_err_thr is not None:
streamlines = FiberUtils.compress_streamlines(
streamlines, compress_err_thr)
FiberUtils.save_streamlines_as_trk(filename_out, streamlines,
reference_affine)
def create_and_run_p3_workflow(imported_workflows, settings):
"""
Create main workflow
"""
# Set nipype debug messages if enabled
if settings['debug']:
config.set('logging', 'workflow_level', 'DEBUG')
config.set('logging', 'workflow_level', 'DEBUG')
# always hash on content
config.set('execution', 'hash_method', 'content')
# stop on first crash
config.set('execution', 'stop_on_first_crash', 'true')
logging.update_logging(config)
# define subworkflows from imported workflows
subworkflows = generate_subworkflows(imported_workflows, settings)
# create a workflow
p3 = Workflow(name='p3_pipeline', base_dir=settings['tmp_dir'])
# get connections
connections = generate_connections(subworkflows, settings)
# connect nodes
p3.connect(connections)
# apply sideloads
sideload_nodes(p3, connections, settings)
# Create graph images
p3.write_graph(os.path.join(settings['output_dir'], 'graph', 'p3'),
graph2use='flat',
simple_form=False)
p3.write_graph(os.path.join(settings['output_dir'], 'graph', 'p3'),
graph2use='colored')
# copy the grpah files to the output directory
# copy2(os.path.join(settings['tmp_dir'],'p3_pipeline','graph.png'),settings['output_dir'])
# copy2(os.path.join(settings['tmp_dir'],'p3_pipeline','graph_detailed.png'),settings['output_dir'])
# Run pipeline (check multiproc setting)
if not settings['disable_run']:
if settings['multiproc']:
p3.run(plugin='MultiProc')
else:
p3.run()
def convert_tck_to_trk(filename_in,
filename_out,
reference_affine,
compress_err_thr=0.1,
smooth=None):
'''
Convert tck file to trk file and compress
:param filename_in:
:param filename_out:
:param compress_err_thr: compress fibers if setting error threshold here (default: 0.1mm)
:param smooth: smooth streamlines (default: None)
10: slight smoothing, 100: very smooth from beginning to end
:return:
'''
#Hide large number of nipype logging outputs
from nipype import config, logging
config.set('execution', 'remove_unnecessary_outputs', 'true')
config.set('logging', 'workflow_level', 'WARNING')
config.set('logging', 'interface_level', 'WARNING')
logging.update_logging(config)
from nipype.interfaces.mrtrix.convert import read_mrtrix_tracks
from dipy.tracking.metrics import spline
hdr, streamlines = read_mrtrix_tracks(
filename_in, as_generator=False) # Load Fibers (Tck)
if smooth is not None:
streamlines_smooth = []
for sl in streamlines:
streamlines_smooth.append(spline(sl, s=smooth))
streamlines = streamlines_smooth
#Compressing also good to remove checkerboard artefacts from tracking on peaks
if compress_err_thr is not None:
streamlines = FiberUtils.compress_streamlines(
streamlines, compress_err_thr)
FiberUtils.save_streamlines_as_trk(filename_out, streamlines,
reference_affine)
# coding: utf-8
# Get the Node and Workflow object
from nipype import Node, Workflow, pipeline
import nipype.interfaces.utility as util # utility (Needed?)
# Specify which SPM to use (useful for the SPM8 comparison testing)
from nipype.interfaces.matlab import MatlabCommand as mlabcmd
mlabcmd.set_default_paths('/usr/local/MATLAB/tools/spm12')
# Use nipype's version of collecting and inputing files.
from nipype import SelectFiles, DataSink, config
#config.enable_debug_mode()
config.set(
'execution', 'stop_on_first_crash', 'true'
) # Doesn't mean the whole pipeline will run properly if set to false, but can run through a couple times and hopefully hit the stragglers. Sometimes, it's because the scan doesn' exist, but the template is not flexible enough to catch it.
#config.set('execution', 'keep_inputs', 'true')
#config.set('execution', 'keep_unnecessary_files','true')
config.set('execution', 'hash_method', 'timestamp')
#config.set('execution', 'poll_sleep_duration','3')
import os
import glob
import os.path as op
# For now, hard code some of the paths that I can use to test the pipeline.
home_dir = op.abspath(
'/data/analysis/brianne/exobk') # Will get from tkinter eventually
paradigm = 'fp' # food pics
output_dir = op.abspath(
from nipype.interfaces.matlab import MatlabCommand
import os
from nipype import config
imports = ['import os',
'import nibabel as nb',
'import numpy as np',
'import scipy as sp',
('from nipype.utils.filemanip import filename_to_list, '
'list_to_filename, split_filename'),
'from scipy.special import legendre'
]
#config.set('execution', 'remove_unnecessary_outputs', 'False')
config.set('execution', 'single_thread_matlab', 'True')
# Specification to MATLAB
MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
fsl.FSLCommand.set_default_output_type('NIFTI')
# Specify Variables
experiment_dir = '/data/eaxfjord/fmriRSWorkingDir/nipype/'
data_dir = os.path.join(experiment_dir, 'data') # location of the data
working_dir = 'working_dir_PreProc_Final'
output_dir = 'output_dir_PreProc3_Final'
# Tissue probability map
tpm = '/usr/local/matlabtools/2014b/spm12/tpm/TPM.nii'
def test_debug_mode():
from ... import logging
sofc_config = config.get('execution', 'stop_on_first_crash')
ruo_config = config.get('execution', 'remove_unnecessary_outputs')
ki_config = config.get('execution', 'keep_inputs')
wf_config = config.get('logging', 'workflow_level')
if_config = config.get('logging', 'interface_level')
ut_config = config.get('logging', 'utils_level')
wf_level = logging.getLogger('nipype.workflow').level
if_level = logging.getLogger('nipype.interface').level
ut_level = logging.getLogger('nipype.utils').level
config.enable_debug_mode()
# Check config is updated and logging levels, too
assert config.get('execution', 'stop_on_first_crash') == 'true'
assert config.get('execution', 'remove_unnecessary_outputs') == 'false'
assert config.get('execution', 'keep_inputs') == 'true'
assert config.get('logging', 'workflow_level') == 'DEBUG'
assert config.get('logging', 'interface_level') == 'DEBUG'
assert config.get('logging', 'utils_level') == 'DEBUG'
assert logging.getLogger('nipype.workflow').level == 10
assert logging.getLogger('nipype.interface').level == 10
assert logging.getLogger('nipype.utils').level == 10
# Restore config and levels
config.set('execution', 'stop_on_first_crash', sofc_config)
config.set('execution', 'remove_unnecessary_outputs', ruo_config)
config.set('execution', 'keep_inputs', ki_config)
config.set('logging', 'workflow_level', wf_config)
config.set('logging', 'interface_level', if_config)
config.set('logging', 'utils_level', ut_config)
logging.update_logging(config)
assert config.get('execution', 'stop_on_first_crash') == sofc_config
assert config.get('execution', 'remove_unnecessary_outputs') == ruo_config
assert config.get('execution', 'keep_inputs') == ki_config
assert config.get('logging', 'workflow_level') == wf_config
assert config.get('logging', 'interface_level') == if_config
assert config.get('logging', 'utils_level') == ut_config
assert logging.getLogger('nipype.workflow').level == wf_level
assert logging.getLogger('nipype.interface').level == if_level
assert logging.getLogger('nipype.utils').level == ut_level
def run_basc_workflow_parallelized(
subject_file_list, roi_mask_file,
dataset_bootstraps_list, timeseries_bootstraps_list, n_clusters_list,
similarity_metric_list, blocklength_list=[1],
cluster_method_list=['ward'],
group_dim_reduce=False, output_size_list=[None],
affinity_threshold_list=[0.0],
cross_cluster=False, cross_cluster_mask_file=None,
out_dir=None, runs=1, proc_mem=None, random_seed=None,
analysis_id='basc', cache_method='content'
):
import os
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
from nipype import config
config.set('execution', 'keep_inputs', 'true')
if cache_method == 'content':
config.set('execution', 'hash_method', 'content')
else:
config.set('execution', 'hash_method', 'timestamp')
from PyBASC.pipeline import create_basc_parallelized
from PyBASC.utils import generate_random_state
if not out_dir:
out_dir = os.getcwd()
analysis_dir = os.path.join(out_dir, analysis_id)
rng = np.random.RandomState(random_seed)
for run_id in range(1, runs + 1):
rng_run = generate_random_state(rng)
workflow = pe.Workflow(name='pipeline')
workflow.base_dir = os.path.join(analysis_dir, 'run_%d' % run_id, 'working')
basc_workflow = create_basc_parallelized(proc_mem, name='basc')
basc_workflow.inputs.inputspec.set(
subjects_files=subject_file_list,
roi_mask_file=roi_mask_file,
group_dim_reduce=group_dim_reduce,
cross_cluster=cross_cluster,
cxc_roi_mask_file=cross_cluster_mask_file,
random_state_tuple=rng_run.get_state()
)
basc_workflow.get_node('inputspec_compression_dim').iterables = [
("compression_dim", output_size_list)
]
basc_workflow.get_node('inputspec_boostraps').iterables = [
('dataset_bootstraps', dataset_bootstraps_list),
('timeseries_bootstraps', timeseries_bootstraps_list),
]
basc_workflow.get_node('inputspec_similarity_metric').iterables = [
('similarity_metric', similarity_metric_list)
]
basc_workflow.get_node('inputspec_cluster_method').iterables = [
('cluster_method', cluster_method_list)
]
basc_workflow.get_node('inputspec_blocklength').iterables = [
('blocklength', blocklength_list)
]
basc_workflow.get_node('inputspec_n_clusters').iterables = [
('n_clusters', n_clusters_list)
]
basc_workflow.get_node('inputspec_affinity_threshold').iterables = [
('affinity_threshold', affinity_threshold_list)
]
resource_pool = {}
resource_pool['group_stability_matrix'] = (basc_workflow, 'outputspec.group_stability_matrix')
resource_pool['clusters_G'] = (basc_workflow, 'outputspec.clusters_G')
resource_pool['ism_gsm_corr'] = (basc_workflow, 'outputspec.ism_gsm_corr')
resource_pool['gsclusters_img'] = (basc_workflow, 'outputspec.gsclusters_img')
#resource_pool['cluster_voxel_scores_img'] = (basc_workflow, 'outputspec.cluster_voxel_scores_img')
#resource_pool['cluster_voxel_scores'] = (basc_workflow, 'outputspec.cluster_voxel_scores')
resource_pool['ind_group_cluster_stability'] = (basc_workflow, 'outputspec.ind_group_cluster_stability')
resource_pool['individualized_group_clusters'] = (basc_workflow, 'outputspec.individualized_group_clusters')
resource_pool['ind_group_cluster_labels'] = (basc_workflow, 'outputspec.ind_group_cluster_labels')
resource_pool['ind_group_cluster_stability_set'] = (basc_workflow, 'outputspec.ind_group_cluster_stability_set')
ds = pe.Node(nio.DataSink(), name='datasink_workflow_name')
ds.inputs.base_directory = os.path.join(analysis_dir, 'run_%d' % run_id)
for output in resource_pool.keys():
node, out_file = resource_pool[output]
workflow.connect(node, out_file, ds, output)
plugin = 'MultiProc'
if int(proc_mem[0]) == 1:
plugin = 'Linear'
plugin_args = {
'n_procs': int(proc_mem[0]),
'memory_gb': int(proc_mem[1])
}
# workflow.write_graph(dotfilename='graph.dot', graph2use='exec')
workflow.run(plugin=plugin, plugin_args=plugin_args)
return analysis_dir
import os
from IPython.display import Image
from nipype.interfaces.base import (
traits,
TraitedSpec,
CommandLineInputSpec,
CommandLine,
File,
isdefined
)
from nipype.utils.filemanip import fname_presuffix
from nipype import config
config.set('execution', 'display_variable', os.environ['DISPLAY'])
print config.get('execution', 'display_variable')
class MP2RageSkullStripInputSpec(CommandLineInputSpec):
in_filter_image = traits.File(mandatory=False, argstr='-inFilter %s', desc=' Filter Image')
in_inv2 = traits.File(exists=True, argstr='-inInv2 %s', desc='Inv2 Image')
in_t1 = traits.File(exists=True, argstr='-inT1 %s', desc='T1 Map image')
int_t1_weighted = traits.File(exists=True, argstr='-inT1weighted %s', desc='T1-Weighted Image')
out_brain_mask = traits.File('brain_mask.nii.gz', usedefault=True, argstr='-outBrain %s', desc='Path/name of brain mask')
out_masked_t1 = traits.File(argstr='-outMasked %s', desc='Create masked T1')
out_masked_t1_weighted = traits.Bool(argstr='-outMasked2 %s', desc='Path/name of masked T1-weighted image')
out_masked_filter_image = traits.Bool(argstr='-outMasked3 %s', desc='Path/name of masked Filter image')
class MP2RageSkullStripOutputSpec(TraitedSpec):
brain_mask = traits.File()
def test_debug_mode():
from ... import logging
sofc_config = config.get('execution', 'stop_on_first_crash')
ruo_config = config.get('execution', 'remove_unnecessary_outputs')
ki_config = config.get('execution', 'keep_inputs')
wf_config = config.get('logging', 'workflow_level')
if_config = config.get('logging', 'interface_level')
ut_config = config.get('logging', 'utils_level')
wf_level = logging.getLogger('nipype.workflow').level
if_level = logging.getLogger('nipype.interface').level
ut_level = logging.getLogger('nipype.utils').level
config.enable_debug_mode()
# Check config is updated and logging levels, too
assert config.get('execution', 'stop_on_first_crash') == 'true'
assert config.get('execution', 'remove_unnecessary_outputs') == 'false'
assert config.get('execution', 'keep_inputs') == 'true'
assert config.get('logging', 'workflow_level') == 'DEBUG'
assert config.get('logging', 'interface_level') == 'DEBUG'
assert config.get('logging', 'utils_level') == 'DEBUG'
assert logging.getLogger('nipype.workflow').level == 10
assert logging.getLogger('nipype.interface').level == 10
assert logging.getLogger('nipype.utils').level == 10
# Restore config and levels
config.set('execution', 'stop_on_first_crash', sofc_config)
config.set('execution', 'remove_unnecessary_outputs', ruo_config)
config.set('execution', 'keep_inputs', ki_config)
config.set('logging', 'workflow_level', wf_config)
config.set('logging', 'interface_level', if_config)
config.set('logging', 'utils_level', ut_config)
logging.update_logging(config)
assert config.get('execution', 'stop_on_first_crash') == sofc_config
assert config.get('execution', 'remove_unnecessary_outputs') == ruo_config
assert config.get('execution', 'keep_inputs') == ki_config
assert config.get('logging', 'workflow_level') == wf_config
assert config.get('logging', 'interface_level') == if_config
assert config.get('logging', 'utils_level') == ut_config
assert logging.getLogger('nipype.workflow').level == wf_level
assert logging.getLogger('nipype.interface').level == if_level
assert logging.getLogger('nipype.utils').level == ut_level
'''
Create nipype workflow using dipy's workflows
https://github.com/nipy/dipy/blob/master/dipy/workflows/
'''
from nipype import config
config.set('execution', 'remove_unnecessary_outputs', 'false')
#config.set('execution', 'crashfile_format', 'txt')
from nipype import Node, Function, Workflow, DataGrabber, IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
import os
from glob import glob
# define inputs
recon = 'csd'
analysis = 'dipy_0.16_workflows_%s'%recon
num_threads = 4
b0_thresh = 80
project_dir = os.path.abspath('/om2/user/ksitek/hcp_7t/')
data_dir = os.path.join(project_dir, 'data')
out_dir = os.path.join(project_dir, 'derivatives', analysis)
work_dir = os.path.join('/om2/scratch/ksitek/hcp/', analysis)
''' define subjects '''
sub_list = [os.path.basename(x) for x in sorted(glob(project_dir+'/data/13*'))]
#sub_list = ['100610'] # test on one subject
''' set up nodes '''
# set up iterables
import nipype.interfaces.freesurfer as fs
import nipype.interfaces.utility as util
from nipype import config
from subject_info import *
import time
import os
from fir_utils import *
study_dir = os.path.abspath('/mindhive/gablab/GATES/Analysis/MCNAB/')
data_dir = os.path.abspath('/mindhive/gablab/GATES/data/')
onsets_dir = os.path.join(data_dir, 'onsets/MCNAB/')
subjects_dir = os.path.abspath('/mindhive/xnat/surfaces/GATES/')
os.environ["SUBJECTS_DIR"] = subjects_dir
fs.FSCommand.set_default_subjects_dir(subjects_dir)
mlab.MatlabCommand.set_default_matlab_cmd("/software/matlab_versions/2010b/bin/matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths('/software/spm8_4290')
config.set('execution','keep_inputs','true')
config.set('execution','remove_unnecessary_outputs','false')
def gen_peak_mask(anat_mask,func_activation):
import nibabel as nb
import numpy as np
from fir_utils import (binarize_peak, dilate_mask)
import sys
import os
anatmask = nb.load(anat_mask)
mask_data = np.asarray(anatmask.get_data())
mask_header = anatmask.get_header()
mask_affine = anatmask.get_affine()
import nipype.interfaces.matlab as mlab # how to run matlab
import nipype.interfaces.spm as spm # spm
import nipype.interfaces.utility as util # utility
import nipype.pipeline.engine as pe # pypeline engine
from nipype.utils.filemanip import loadflat # some useful stuff for debugging
import scipy.io as sio
import numpy as np
from nipype.interfaces.base import Bunch
from copy import deepcopy
import sys
from nipype import config
from compcor_workflow import create_compcorr, extract_noise_components
from subject_info import info, subject_list
config.set("execution", "keep_inputs", "true")
config.set("execution", "remove_unnecessary_outputs", "false")
##############################################################################
# ARGUMENTS
##############################################################################
# perhaps implement with argparse
print sys.argv
if not len(sys.argv) == 4:
sys.stderr.write("The paradigm and analysis level must be provided on the command line")
sys.exit("The paradigm and analysis level must be provided on the command line")
elif sys.argv[1] in ["WMSTAT", "WM", "Nback_spatial", "Nback_letters", "MCNAB"]:
# originally 'condition'
paradigm = sys.argv[1]
if sys.argv[2] in ["l1", "l2"]:
levelToRun = sys.argv[2]
else:
parser.add_argument("--work_dir", type=str, required=True)
parser.add_argument("--out_dir", type=str, required=True)
parser.add_argument('--debug',
dest='debug',
action='store_true',
help='debug mode')
args = parser.parse_args()
if args.debug:
from nipype import config
config.enable_debug_mode()
config.set('execution', 'stop_on_first_crash', 'true')
config.set('execution', 'remove_unnecessary_outputs', 'false')
config.set('execution', 'keep_inputs', 'true')
config.set('logging', 'workflow_level', 'DEBUG')
config.set('logging', 'interface_level', 'DEBUG')
config.set('logging', 'utils_level', 'DEBUG')
wf = create_workflow(xfm_dir=os.path.abspath(args.xfm_dir),
xfm_pattern=args.xfm_pattern,
atlas_dir=os.path.abspath(args.atlas_dir),
atlas_pattern=args.atlas_pattern,
source_dir=os.path.abspath(args.source_dir),
source_pattern=args.source_pattern,
work_dir=os.path.abspath(args.work_dir),
out_dir=os.path.abspath(args.out_dir),
name=args.name)
def main(model_dir=MODEL_DIR, subid_label='mrishare_id', design_input='fsgd'):
'''
Runs the WF for all the groups found in model dir.
'''
wd = "/beegfs_data/scratch/tsuchida-SBM"
fs_subdir = '/data/analyses/work_in_progress/freesurfer/fsmrishare-flair6.0/'
# find the groups in group dirs
group_csv_glob = glob.glob(op.join(model_dir, '*', 'group_info.csv'))
print('Found {} group_info.csv...'.format(len(group_csv_glob)))
if not group_csv_glob:
raise Exception('No group info found in the model dir')
for group_info_path in group_csv_glob:
group_info = pd.read_csv(group_info_path)
group_name = group_info_path.split('/')[-2]
group_dir = op.dirname(group_info_path)
# Copy the models to wd
wd_indir = op.join(wd, 'input_dir')
os.makedirs(wd_indir, exist_ok=True)
group_indir = op.join(wd_indir, group_name)
sp.call(['rsync', '-avh', '{}/'.format(group_dir), group_indir])
# get the model name list
model_dirs = glob.glob(op.join(group_dir, 'Model*/'))
if not model_dirs:
print('No Model dirs found for the group {}'.format(group_name))
break
model_names = [m.split('/')[-2] for m in sorted(model_dirs)]
print('Found {} following models fround for the group {}'.format(
len(model_names), group_name))
print('Gathering contrast info for each model...')
model_info = {}
for model_name, model_dir in zip(model_names, model_dirs):
contrast_files = glob.glob(op.join(model_dir, '*.mtx'))
if contrast_files:
contrast_names = [
op.basename(f).replace('.mtx', '') for f in contrast_files
]
print('{}: {}'.format(model_name, contrast_names))
for f, name in zip(contrast_files, contrast_names):
sign_file = f.replace('.mtx', '.mdtx')
if not op.exists(sign_file):
raise Exception(
'Could not find corresponding sign file for contrast {}'
.format(name))
else:
model_info[model_name] = 'dods'
# log dir
log_dir = op.join(os.getcwd(), 'log_dir', group_name)
os.makedirs(log_dir, exist_ok=True)
# WF for the group
group_sublist = group_info[subid_label].values.tolist()
wf = genFreesurferSBMglmWF(name='SBM_{}'.format(group_name),
base_dir=wd,
group_sublist=group_sublist,
model_dir=group_indir,
model_info=model_info,
design_input=design_input,
fs_subjects_dir=fs_subdir,
fwhm=[0.0, 10.0],
measure_list=['thickness', 'area'],
target_atlas='fsaverage',
target_atlas_surfreg='sphere.reg',
correction_method='FDR')
config.update_config(
{'logging': {
'log_directory': log_dir,
'log_to_file': True
}})
logging.update_logging(config)
config.set('execution', 'job_finished_timeout', '20.0')
config.set('execution', 'keep_inputs', 'true')
wf.run(
plugin='SLURM',
plugin_args={ #'sbatch_args': '--partition=gindev',
'dont_resubmit_completed_jobs': True,
'max_jobs': 50
})
def test_debug_mode():
from ... import logging
sofc_config = config.get("execution", "stop_on_first_crash")
ruo_config = config.get("execution", "remove_unnecessary_outputs")
ki_config = config.get("execution", "keep_inputs")
wf_config = config.get("logging", "workflow_level")
if_config = config.get("logging", "interface_level")
ut_config = config.get("logging", "utils_level")
wf_level = logging.getLogger("nipype.workflow").level
if_level = logging.getLogger("nipype.interface").level
ut_level = logging.getLogger("nipype.utils").level
config.enable_debug_mode()
# Check config is updated and logging levels, too
assert config.get("execution", "stop_on_first_crash") == "true"
assert config.get("execution", "remove_unnecessary_outputs") == "false"
assert config.get("execution", "keep_inputs") == "true"
assert config.get("logging", "workflow_level") == "DEBUG"
assert config.get("logging", "interface_level") == "DEBUG"
assert config.get("logging", "utils_level") == "DEBUG"
assert logging.getLogger("nipype.workflow").level == 10
assert logging.getLogger("nipype.interface").level == 10
assert logging.getLogger("nipype.utils").level == 10
# Restore config and levels
config.set("execution", "stop_on_first_crash", sofc_config)
config.set("execution", "remove_unnecessary_outputs", ruo_config)
config.set("execution", "keep_inputs", ki_config)
config.set("logging", "workflow_level", wf_config)
config.set("logging", "interface_level", if_config)
config.set("logging", "utils_level", ut_config)
logging.update_logging(config)
assert config.get("execution", "stop_on_first_crash") == sofc_config
assert config.get("execution", "remove_unnecessary_outputs") == ruo_config
assert config.get("execution", "keep_inputs") == ki_config
assert config.get("logging", "workflow_level") == wf_config
assert config.get("logging", "interface_level") == if_config
assert config.get("logging", "utils_level") == ut_config
assert logging.getLogger("nipype.workflow").level == wf_level
assert logging.getLogger("nipype.interface").level == if_level
assert logging.getLogger("nipype.utils").level == ut_level
from sfDM.vis import colormaps
from sfDM.vis.map_maker import MapMaker
import re
from nipype import config
fsl.FSLCommand.set_default_output_type('NIFTI')
#Data set up
config_file = os.environ['fdm_config']
timeline_file = os.environ['fdm_timeline']
with open(config_file, 'r') as f:
cfg = json.load(f)
parent = cfg['parent_dir']
config.set('execution', 'crashdump_dir', parent)
with open(timeline_file, 'r') as f:
tmln = json.load(f)
outputdir = parent + '/FDM/Outputs/'
#-----------------------Begin Map Calculations--------------------------------------------------------------------#
fsl.FSLCommand.set_default_output_type('NIFTI')
scan_list = [
"scan_{0:0>2d}".format(x + 1) for x in range(cfg['number_of_scans'])
]
print scan_list
'''
After creating tractography streamlines with dipy_csd.py,
this workflow takes an atlas file and finds connections
between each region in the atlas
KRS 2018.05.04
'''
from nipype import config
config.set('execution', 'remove_unnecessary_outputs', 'false')
config.set('execution', 'crashfile_format', 'txt')
#config.enable_provenance()
from nipype import Node, Function, Workflow, IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
import os
from glob import glob
# which data sampling? also used for naming
resolution = '1.0mm'
out_prefix = 'fathresh-0.1'
data_dir = os.path.abspath('/om2/user/ksitek/exvivo/data')
out_dir = os.join('/om2/user/ksitek/exvivo/analysis/dipy_csd/',
'%s_%s_anat-atlas/' % (out_prefix, resolution))
sids = ['Reg_S64550']
if not os.path.exists(out_dir):
os.mkdir(out_dir)
work_dir = os.path.abspath('/om2/scratch/ksitek/dipy_csd/%s_%s/' %
def run_basc_workflow(
subject_file_list, roi_mask_file,
dataset_bootstraps, timeseries_bootstraps, n_clusters, output_size,
bootstrap_list, proc_mem, similarity_metric, group_dim_reduce=False,
cross_cluster=False, cross_cluster_mask_file=None, blocklength=1,
affinity_threshold=0.0, cluster_method='ward', out_dir=None, run=True
):
"""Run the 'template_workflow' function to execute the modular workflow
with the provided inputs.
:type input_resource: str
:param input_resource: The filepath of the { input resource }. Can have
multiple.
:type out_dir: str
:param out_dir: (default: None) The output directory to write the results
to; if left as None, will write to the current directory.
:type run: bool
:param run: (default: True) Will run the workflow; if set to False, will
connect the Nipype workflow and return the workflow object
instead.
:rtype: str
:return: (if run=True) The filepath of the generated anatomical_reorient
file.
:rtype: Nipype workflow object
:return: (if run=False) The connected Nipype workflow object.
:rtype: str
:return: (if run=False) The base directory of the workflow if it were to
be run.
"""
import os
import glob
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
from PyBASC.pipeline import create_basc
from nipype import config
config.set('execution', 'keep_inputs', 'true')
workflow = pe.Workflow(name='basc_workflow_runner')
if not out_dir:
out_dir = os.getcwd()
workflow.base_dir = out_dir
resource_pool = {}
basc = create_basc(proc_mem, name='basc')
basc.inputs.inputspec.set(
subjects_files=subject_file_list,
roi_mask_file=roi_mask_file,
dataset_bootstraps=dataset_bootstraps,
timeseries_bootstraps=timeseries_bootstraps,
n_clusters=n_clusters,
compression_dim=output_size,
bootstrap_list=bootstrap_list,
similarity_metric=similarity_metric,
group_dim_reduce=group_dim_reduce,
cross_cluster=cross_cluster,
cxc_roi_mask_file=cross_cluster_mask_file,
blocklength=blocklength,
affinity_threshold=affinity_threshold,
cluster_method=cluster_method
)
resource_pool['group_stability_matrix'] = (basc, 'outputspec.group_stability_matrix')
resource_pool['clusters_G'] = (basc, 'outputspec.clusters_G')
resource_pool['ism_gsm_corr'] = (basc, 'outputspec.ism_gsm_corr')
resource_pool['gsclusters_img'] = (basc, 'outputspec.gsclusters_img')
#resource_pool['cluster_voxel_scores_img'] = (basc, 'outputspec.cluster_voxel_scores_img')
#resource_pool['cluster_voxel_scores'] = (basc, 'outputspec.cluster_voxel_scores')
resource_pool['ind_group_cluster_stability'] = (basc, 'outputspec.ind_group_cluster_stability')
resource_pool['individualized_group_clusters'] = (basc, 'outputspec.individualized_group_clusters')
resource_pool['ind_group_cluster_labels'] = (basc, 'outputspec.ind_group_cluster_labels')
resource_pool['ind_group_cluster_stability_set'] = (basc, 'outputspec.ind_group_cluster_stability_set')
ds = pe.Node(nio.DataSink(), name='datasink_workflow_name')
ds.inputs.base_directory = out_dir
for output in resource_pool.keys():
node, out_file = resource_pool[output]
workflow.connect(node, out_file, ds, output)
plugin = 'MultiProc'
if int(proc_mem[0]) == 1:
plugin = 'Linear'
plugin_args = {
'n_procs': int(proc_mem[0]),
'memory_gb': int(proc_mem[1])
}
workflow.run(plugin=plugin, plugin_args=plugin_args)
outpath = glob.glob(os.path.join(out_dir, "*", "*"))
return outpath
#!/usr/bin/env python
import os
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as pe
from nipype import config, logging
config.set("logging", "filemanip_level", 'DEBUG')
logging.update_logging(config)
realign = pe.Node(spm.Realign(), name='realign')
realign.inputs.in_files = [
os.path.abspath(p) for p in ['src/bold_run1.img', 'src/bold_run2.img']
]
realign.inputs.jobtype = 'estwrite'
realign.inputs.write_which = [0, 1]
coregister = pe.Node(spm.Coregister(), name='coregister')
coregister.inputs.target = os.path.abspath('src/highres001.img')
coregister.inputs.jobtype = 'estimate'
wf = pe.Workflow(name='spm_preproc')
wf.base_dir = './nipype-workingdir'
wf.connect([
(realign, coregister,
[('modified_in_files', 'apply_to_files'),
('mean_image', 'source')]),
])
from sfDM.vis.map_maker import MapMaker
import re
from nipype import config
fsl.FSLCommand.set_default_output_type('NIFTI')
#Data set up
config_file = os.environ['fdm_config']
timeline_file = os.environ['fdm_timeline']
with open(config_file, 'r') as f:
cfg = json.load(f)
parent = cfg['parent_dir']
config.set('execution','crashdump_dir',parent)
with open(timeline_file, 'r') as f:
tmln = json.load(f)
outputdir=parent+'/FDM/Outputs/'
#-----------------------Begin Map Calculations--------------------------------------------------------------------#
fsl.FSLCommand.set_default_output_type('NIFTI')
scan_list = ["scan_{0:0>2d}".format(x+1) for x in range(cfg['number_of_scans'])]
print scan_list
Tumor=[outputdir+'Tumor/'+scan+'/'+(os.listdir(outputdir+'Tumor/'+scan)[0]) for scan in scan_list]
input_dir = sys.argv[3]
sub_file = sys.argv[4]
atlas = sys.argv[5]
grid = sys.argv[6]
subjects = pickle.load(open(sub_file, "rb"))
wf = Lesion_extractor(
wf_name=wf_name,
base_dir=base_dir,
input_dir=input_dir,
subjects=subjects,
#main=main,
#acc=acc,
atlas=atlas)
config.update_config(
{'logging': {
'log_directory': wf.base_dir,
'log_to_file': True
}})
logging.update_logging(config)
config.set('execution', 'job_finished_timeout', '20.0')
wf.config['execution'] = {'job_finished_timeout': '10.0'}
try:
wf.run('SLURM', plugin_args={'sbatch_args': '-p ' + grid})
#wf.write_graph(dotfilename='WAIMEA', graph2use='colored', format='png', simple_form=True)
except:
print('Error! Pipeline exited with exception:')
raise
def test_PathReferenceTracer_indirect_refs(tmp_path):
from nipype import config
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
os.chdir(str(tmp_path))
config.set_default_config()
config.set("execution", "remove_unnecessary_outputs", False)
wf = pe.Workflow("w", base_dir=Path.cwd())
x = pe.Node(interface=niu.Function(function=totxt,
input_names=["a", "b"],
output_names=["c", "d"]),
name="x")
x.inputs.a = 1
x.inputs.b = 2
y = pe.Node(interface=niu.Function(function=select,
input_names=["a", "b"],
output_names=["c"]),
name="y")
wf.connect(x, "c", y, "a")
wf.connect(x, "d", y, "b")
z = pe.Node(interface=niu.Function(function=select,
input_names=["a", "b"],
output_names=["c"]),
name="z")
wf.connect(y, "c", z, "a")
wf.connect(y, "c", z, "b")
execgraph = wf.run(plugin=DontRunRunner())
def get_node(name):
for node in execgraph.nodes():
if node.name == name:
return node
x = get_node("x")
y = get_node("y")
z = get_node("z")
rt = PathReferenceTracer()
for node in execgraph.nodes:
rt.add_node(node)
for node in execgraph.nodes:
rt.set_node_pending(node)
xrf = rt.node_resultfile_path(x)
yrf = rt.node_resultfile_path(y)
zrf = rt.node_resultfile_path(z)
result = x.run()
rt.set_node_complete(x, True)
c = result.outputs.c
d = result.outputs.d
assert rt.refs[xrf] == set([yrf])
assert rt.refs[yrf] == set([zrf])
assert rt.refs[zrf] == set([])
assert rt.refs[c] == set([xrf])
assert rt.refs[d] == set([xrf])
assert rt.deps[xrf] == set([xrf.parent, c, d])
assert rt.deps[yrf] == set([yrf.parent, xrf])
assert rt.deps[zrf] == set([zrf.parent, yrf])
assert rt.deps[c] == set([xrf.parent])
assert rt.deps[d] == set([xrf.parent])
y.run()
rt.set_node_complete(y, True)
assert rt.refs[xrf] == set([])
assert rt.refs[yrf] == set([zrf])
assert rt.refs[zrf] == set([])
assert rt.refs[c] == set([xrf])
assert rt.refs[d] == set([xrf, yrf])
assert rt.deps[xrf] == set([xrf.parent, c, d])
assert rt.deps[yrf] == set([yrf.parent, d])
assert rt.deps[zrf] == set([zrf.parent, yrf])
assert rt.deps[c] == set([xrf.parent])
assert rt.deps[d] == set([xrf.parent])
rtc = set(rt.collect())
assert rtc == set([xrf, c])
if not isdefined(self.inputs.out_file):
outputs["resized_file"] = self._gen_filename(self.inputs.in_file)
else:
outputs["resized_file"] = os.path.abspath(self.inputs.out_file)
return outputs
def _gen_filename(self, name):
pth, fn, ext = filemanip.split_filename(self.inputs.in_file)
return os.path.join(os.getcwd(), fn + "_resized" + ext)
from nipype.utils.filemanip import fname_presuffix
from nipype import config
config.set("execution", "display_variable", os.environ["DISPLAY"])
print config.get("execution", "display_variable")
class MP2RageSkullStripInputSpec(CommandLineInputSpec):
in_filter_image = traits.File(mandatory=False, argstr="-inFilter %s", desc=" Filter Image")
in_inv2 = traits.File(exists=True, argstr="-inInv2 %s", desc="Inv2 Image")
in_t1 = traits.File(exists=True, argstr="-inT1 %s", desc="T1 Map image")
in_t1_weighted = traits.File(exists=True, argstr="-inT1weighted %s", desc="T1-Weighted Image")
out_brain_mask = traits.File(
"brain_mask.nii.gz", usedefault=True, argstr="-outBrain %s", desc="Path/name of brain mask"
)
out_masked_t1 = traits.Bool(True, usedefault=True, argstr="-outMasked %s", desc="Create masked T1")
out_masked_t1_weighted = traits.Bool(
True, usedefault=True, argstr="-outMasked2 %s", desc="Path/name of masked T1-weighted image"
)
