def aseg_to_tissuemaps(aseg):
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nibabel import load, save, Nifti1Image
from numpy import zeros_like
from os.path import abspath
aseg_nifti = load(aseg)
aseg_data = aseg_nifti.get_data()
cortical_labels = [3, 42]
subcortical_labels =[8, 10, 11, 12, 13, 17, 18, 26, 47, 49, 50, 51, 52, 53, 54, 58]
#creating array of zeroes that replaces 0's with 1's when matches values of subcortical_labels
cortical_data = zeros_like(aseg_data)
for x in cortical_labels:
cortical_data[aseg_data == x] = 1
cortical_nifti = Nifti1Image(cortical_data, aseg_nifti.affine)
subcort_data = zeros_like(aseg_data)
for x in subcortical_labels:
subcort_data[aseg_data == x] = 1
subcort_nifti = Nifti1Image(subcort_data, aseg_nifti.affine)
save(subcort_nifti, "subcortical_gm.nii.gz")
save(cortical_nifti, "cortical_gm.nii.gz")
subcort_file = abspath("subcortical_gm.nii.gz")
cortical_file = abspath("cortical_gm.nii.gz")
gm_list = [subcort_file, cortical_file]
return(gm_list)
def check_mask_coverage(epi,brainmask):
from os.path import abspath
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nilearn import plotting
from nipype.interfaces.nipy.preprocess import Trim
trim = Trim()
trim.inputs.in_file = epi
trim.inputs.end_index = 1
trim.inputs.out_file = 'epi_vol1.nii.gz'
trim.run()
epi_vol = abspath('epi_vol1.nii.gz')
maskcheck_filename='maskcheck.png'
display = plotting.plot_anat(epi_vol, display_mode='ortho',
draw_cross=False,
title = 'brainmask coverage')
display.add_contours(brainmask,levels=[.5], colors='r')
display.savefig(maskcheck_filename)
display.close()
maskcheck_file = abspath(maskcheck_filename)
return(maskcheck_file)
def make3DTemplate(subject_T1s, num_proc, output_prefix):
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from os.path import abspath, split
from os import getcwd
from shutil import copyfile
from glob import glob
from subprocess import call
curr_dir = getcwd()
#copy T1s into current directory
for T in range(0,len(subject_T1s)):
[dirname,filename] = split(subject_T1s[T])
copyfile(subject_T1s[T],curr_dir + '/S' + str(T)+'_'+filename)
# -c flag is control for local computing (2= use localhost; required for -j flag)
# -j flag is for number of processors allowed
call(['antsMultivariateTemplateConstruction2.sh', '–d','3','–o', output_prefix,'–r','1','–c','2','–j', str(num_proc), '*.nii.gz'])
sample_template = abspath(output_prefix + 'template0.nii.gz')
return(sample_template)
def adjust_masks(masks):
from os.path import abspath
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nipype.interfaces.freesurfer.model import Binarize
#pve0 = csf, pve1 = gm, pve2 = wm
origvols = sorted(masks)
csf = origvols[0]
wm = origvols[2]
erode = Binarize()
erode.inputs.in_file = wm
erode.inputs.erode = 1
erode.inputs.min = 0.5
erode.inputs.max = 1000
erode.inputs.binary_file = 'WM_seg.nii'
erode.run()
wm_new = abspath(erode.inputs.binary_file)
vols = []
vols.append(wm_new)
vols.append(csf)
return (vols)
def sort_pes(pes):
from nipype import config, logging
from nipype.interfaces.fsl import Merge
from os.path import abspath
config.enable_debug_mode()
logging.update_logging(config)
print(pes)
pe1s = []
pe0s = []
for file in pes:
if 'pe0' in file:
pe0s.append(file)
elif 'pe1' in file:
pe1s.append(file)
pe1s = sorted(pe1s)
pe0s = sorted(pe0s)
me = Merge()
merged_pes = []
for i in range(0,len(pe1s)):
num=pe1s[i][-12:-11]
me.inputs.in_files = [pe1s[i],pe0s[i]]
me.inputs.dimension='t'
me.inputs.merged_file = 'merged_pes%s.nii.gz' % num
me.run()
file = abspath('merged_pes%s.nii.gz' % num)
merged_pes.append(file)
return(merged_pes)
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 setup_environment(argv):
print("Configuring environment...")
import os
import os.path
from BAW.utilities.configFileParser import resolveDataSinkOption, parseFile
from BAW.utilities.pathHandling import validatePath
from BAW.utilities import misc
from collections import OrderedDict # Need OrderedDict internally to ensure consistent ordering
environment, experiment, pipeline, cluster = parseFile(
argv["--ExperimentConfig"], argv["--pe"], argv["--workphase"])
pipeline['ds_overwrite'] = resolveDataSinkOption(argv, pipeline)
if cluster is None:
print("Running on local")
# raise NotImplementedError("Running local has old code and has not been tested!")
# assert argv["--wfrun"] in argvWFRUN, \
# "wfrun options for clusters can only be given when the configuration file's CLUSTER option == True"
# os.environ['NSLOTS'] = str(misc.get_cpus(argv["--wf_template_runner"]))
else:
load_modules(cluster['modules']) # Load modules if not already done ## MODS PATH
# print os.environ['LOADEDMODULES']
# if environment['virtualenv_dir'] is not None: # MODS PATH
# activate_this = validatePath(
# os.path.join(environment['virtualenv_dir'], 'bin', 'activate_this.py'), False, False)
# if os.path.exists( activate_this ) :
# exec(open(activate_this).read(), OrderedDict(__file__=activate_this))
utilities_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'utilities')
configure_env = validatePath(os.path.join(utilities_path, 'configure_env.py'), False, False)
# Add the AutoWorkup directory to the PYTHONPATH every time - REQUIRED FOR CLUSTER DISPATCHING
environment['env']['PYTHONPATH'] = environment['env']['PYTHONPATH'] + ":" + os.path.dirname(__file__)
exec(open(configure_env).read(), OrderedDict(__file__=__file__,
append_os_path=environment['env']['PATH'],
append_sys_path=environment['env']['PYTHONPATH'])
) # MODS PATH
print(("@" * 80))
print((environment['env']['PYTHONPATH']))
print(("@" * 80))
print((environment['env']['PATH']))
print(("@" * 80))
from nipype import config
config.enable_debug_mode()
# config.enable_provenance()
from BAW.utilities.package_check import verify_packages
verify_packages()
if 'FREESURFER' in experiment['components']: # FREESURFER MODS
configure_FS = validatePath(os.path.join(utilities_path, 'utilities', 'configure_FS.py'), False, False)
exec(open(configure_FS).read(), OrderedDict(FS_VARS=misc.FS_VARS, env=environment['env']))
print("FREESURFER needs to check for sane environment here!") # TODO: raise warning, write method, what???
for key, value in list(environment['env'].items()):
if key in ['PATH', 'PYTHONPATH'] + misc.FS_VARS:
pass
else:
os.environ[key] = value # Do not use os.putenv (see Python documentation)
return environment, experiment, pipeline, cluster
def setup(argv):
print "Configuring environment..."
import os, os.path
from utilities.configFileParser import resolveDataSinkOption, parseFile
from utilities.pathHandling import validatePath
from utilities import misc
environment, experiment, pipeline, cluster = parseFile(
argv["--ExperimentConfig"], argv["--pe"])
pipeline['ds_overwrite'] = resolveDataSinkOption(argv, pipeline)
if cluster is None:
assert argv["--wfrun"] in misc.WFRUN, \
"wfrun options for clusters can only be given when the configuration file's CLUSTER option == True"
os.environ['NSLOTS'] = str(misc.get_cpus(argv["--wfrun"]))
else:
load_modules(cluster['modules']
) # Load modules if not already done ## MODS PATH
# print os.environ['LOADEDMODULES']
if environment['virtualenv_dir']: ## MODS PATH
activate_this = validatePath(
os.path.join(environment['virtualenv_dir'], 'bin',
'activate_this.py'), False, False)
execfile(activate_this, dict(__file__=activate_this))
utilities_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'utilities')
configure_env = validatePath(
os.path.join(utilities_path, 'configure_env.py'), False, False)
# Add the AutoWorkup directory to the PYTHONPATH every time - REQUIRED FOR CLUSTER DISPATCHING
environment['env']['PYTHONPATH'] = environment['env'][
'PYTHONPATH'] + ":" + os.path.dirname(__file__)
execfile(
configure_env,
dict(__file__=__file__,
append_os_path=environment['env']['PATH'],
append_sys_path=environment['env']['PYTHONPATH'])) ## MODS PATH
from nipype import config
config.enable_debug_mode()
from utilities.package_check import verify_packages
verify_packages()
if 'FREESURFER' in experiment['components']: ## FREESURFER MODS
configure_FS = validatePath(
os.path.join(utilities_path, 'utilities', 'configure_FS.py'),
False, False)
execfile(configure_FS,
dict(FS_VARS=misc.FS_VARS, env=environment['env']))
print "FREESURFER needs to check for sane environment here!" # TODO: raise warning, write method, what???
for key, value in environment['env'].items():
if key in ['PATH', 'PYTHONPATH'] + misc.FS_VARS:
pass
else:
os.environ[
key] = value # Do not use os.putenv (see Python documentation)
return environment, experiment, pipeline, cluster
def combine_masks(mask1,mask2):
from nipype.interfaces.fsl.utils import Merge
from os.path import abspath
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
vols = []
vols.append(mask1)
vols.append(mask2)
return(vols)
def extract_fisherZ(subj_betas, clusters, cluster_table):
from os import path
from numpy import genfromtxt, savetxt
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
header = []
clusters = genfromtxt(clusters_table, delimiter='\t', dtype=None, skip_header=1)
savetxt(file, matrix, delimiter='\t', header=header)
return(table_path)
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 convertafni(in_file):
from nipype.interfaces.afni.utils import AFNItoNIFTI
from os import path
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
cvt = AFNItoNIFTI()
cvt.inputs.in_file = in_file
cvt.inputs.out_file = 'func_filtered.nii.gz'
cvt.run()
out_file = path.abspath('func_filtered.nii.gz')
return(out_file)
def relabel_fast(fast_tissue_list):
from nipype import config, logging
from os.path import split
from os import rename
config.enable_debug_mode()
logging.update_logging(config)
tissue_list = sorted(fast_tissue_list)
csf = tissue_list[0]
wm = tissue_list[2]
[wd, csf_file] = split(csf)
[wd, wm_file] = split(wm)
rename(csf, wd + 'csf.nii.gz')
rename(wm, wd + 'wm.nii.gz')
wm_csf = [wd + 'csf.nii.gz', wd + 'wm.nii.gz']
return(wm_csf)
def determine_clusters(clusters_table, min_clust_size):
from os import path
from numpy import genfromtxt
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
clusters = genfromtxt(clusters_table, delimiter='\t', dtype=None, skip_header=1)
clusters_to_extract = []
for t in clusters:
if clusters[t][1] >= min_clust_size:
clusters_to_extract.append(clusters[t][0])
return(cluster_index)
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 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 main(argv=None):
import os
import sys
from nipype import config
config.enable_debug_mode()
#-------------------------------- argument parser
import argparse
argParser = argparse.ArgumentParser( description="""****************************
10-cross validation analysis
""")
# workup arguments
argWfGrp = argParser.add_argument_group( 'argWfGrp', """****************************
auto workflow arguments for cross validation
""")
argWfGrp.add_argument('--experimentalConfigurationFile',
help="""experimentalConfigurationFile
Configuration file name with FULL PATH""",
dest='experimentalConfigurationFile', required=True)
argWfGrp.add_argument( '--expDir', help="""expDir
""",
dest='expDir', required=False, default=".")
argWfGrp.add_argument( '--baseDir', help="""baseDir
""",
dest='baseDir', required=False, default=".")
argWfGrp.add_argument( '--runOption', help="""runOption [local/cluster]
""",
dest='runOption', required=False, default="local")
argWfGrp.add_argument( '--PythonBinDir', help="""PythonBinDir [local/cluster]
""",
dest='PythonBinDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsSrcDir', help="""BRAINSToolsSrcDir [local/cluster]
""",
dest='BRAINSToolsSrcDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsBuildDir', help="""BRAINSToolsBuildDir [local/cluster]
""",
dest='BRAINSToolsBuildDir', required=False, default="NA")
args = argParser.parse_args()
similarityComputeWorkflow(args.expDir,
args.baseDir,
args.experimentalConfigurationFile,
args.runOption,
args.PythonBinDir,
args.BRAINSToolsSrcDir,
args.BRAINSToolsBuildDir)
def main(argv=None):
import os
import sys
from nipype import config
config.enable_debug_mode()
#-------------------------------- argument parser
import argparse
argParser = argparse.ArgumentParser( description="""****************************
10-cross validation analysis
""")
# workup arguments
argWfGrp = argParser.add_argument_group( 'argWfGrp', """****************************
auto workflow arguments for cross validation
""")
argWfGrp.add_argument('--experimentalConfigurationFile',
help="""experimentalConfigurationFile
Configuration file name with FULL PATH""",
dest='experimentalConfigurationFile', required=True)
argWfGrp.add_argument( '--expDir', help="""expDir
""",
dest='expDir', required=False, default=".")
argWfGrp.add_argument( '--baseDir', help="""baseDir
""",
dest='baseDir', required=False, default=".")
argWfGrp.add_argument( '--runOption', help="""runOption [local/cluster]
""",
dest='runOption', required=False, default="local")
argWfGrp.add_argument( '--PythonBinDir', help="""PythonBinDir [local/cluster]
""",
dest='PythonBinDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsSrcDir', help="""BRAINSToolsSrcDir [local/cluster]
""",
dest='BRAINSToolsSrcDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsBuildDir', help="""BRAINSToolsBuildDir [local/cluster]
""",
dest='BRAINSToolsBuildDir', required=False, default="NA")
args = argParser.parse_args()
similarityComputeWorkflow(args.expDir,
args.baseDir,
args.experimentalConfigurationFile,
args.runOption,
args.PythonBinDir,
args.BRAINSToolsSrcDir,
args.BRAINSToolsBuildDir)
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_examples(example, pipelines, plugin):
print 'running example: %s with plugin: %s'%(example, plugin)
from nipype import config
config.enable_debug_mode()
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'}}
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, plugin):
print('running example: %s with plugin: %s' % (example, plugin))
from nipype import config
config.enable_debug_mode()
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'}}
wf.run(plugin=plugin, plugin_args={'n_procs': 4})
# run twice to check if nothing is rerunning
wf.run(plugin=plugin)
def brightthresh(func):
import nibabel as nib
from numpy import median, where
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
func_nifti1 = nib.load(func)
func_data = func_nifti1.get_data()
func_data = func_data.astype(float)
brain_values = where(func_data > 0)
median_thresh = median(brain_values)
bright_thresh = 0.75 * median_thresh
return(bright_thresh)
def prep_logging(opts, output_folder):
cli_file = f'{output_folder}/rabies_{opts.rabies_stage}.pkl'
if os.path.isfile(cli_file):
raise ValueError(f"""
A previous run was indicated by the presence of {cli_file}.
This can lead to inconsistencies between previous outputs and the log files.
To prevent this, you are required to manually remove {cli_file}, and we
recommend also removing previous datasinks from the {opts.rabies_stage} RABIES step.
""")
with open(cli_file, 'wb') as handle:
pickle.dump(opts, handle, protocol=pickle.HIGHEST_PROTOCOL)
# remove old versions of the log if already existing
log_path = f'{output_folder}/rabies_{opts.rabies_stage}.log'
if os.path.isfile(log_path):
os.remove(log_path)
config.update_config({'logging': {'log_directory': output_folder,
'log_to_file': True}})
# setting workflow logging level
if opts.verbose==0:
level="WARNING"
elif opts.verbose==1:
level="INFO"
elif opts.verbose<=2:
level="DEBUG"
config.enable_debug_mode()
else:
raise ValueError(f"--verbose must be provided an integer of 0 or above. {opts.verbose} was provided instead.")
# nipype has hard-coded 'nipype.log' filename; we rename it after it is created, and change the handlers
logging.update_logging(config)
os.rename(f'{output_folder}/pypeline.log', log_path)
# change the handlers path to the desired file
for logger in logging.loggers.keys():
log = logging.getLogger(logger)
handler = log.handlers[0]
handler.baseFilename = log_path
# set the defined level of verbose
log = logging.getLogger('nipype.workflow')
log.setLevel(level)
log.debug('Debug ON')
return log
def combine_par(par_list):
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from os.path import abspath
from numpy import vstack, savetxt, genfromtxt
motion = genfromtxt(par_list[0], dtype=float)
if len(par_list)>1:
for file in par_list[1:]:
temp = genfromtxt(par_list[0], dtype=float)
motion=vstack((motion,temp))
filename = 'motion.par'
savetxt(filename, motion, delimiter=' ')
combined_par = abspath(filename)
return(combined_par)
def brightthresh(func):
import nibabel as nib
from numpy import median, where
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
func_nifti1 = nib.load(func)
func_data = func_nifti1.get_data()
func_data = func_data.astype(float)
brain_values = where(func_data > 0)
median_thresh = median(brain_values)
bright_thresh = 0.75 * median_thresh
return (bright_thresh)
def create_coreg_plot(epi,anat):
import os
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nilearn import plotting
coreg_filename='coregistration.png'
display = plotting.plot_anat(epi, display_mode='ortho',
draw_cross=False,
title = 'coregistration to anatomy')
display.add_edges(anat)
display.savefig(coreg_filename)
display.close()
coreg_file = os.path.abspath(coreg_filename)
return(coreg_file)
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 create_coreg_plot(epi, anat):
import os
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nilearn import plotting
coreg_filename = 'coregistration.png'
display = plotting.plot_anat(epi,
display_mode='ortho',
draw_cross=False,
title='coregistration to anatomy')
display.add_edges(anat)
display.savefig(coreg_filename)
display.close()
coreg_file = os.path.abspath(coreg_filename)
return (coreg_file)
def check_mask_coverage(epi, brainmask):
import os
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from nilearn import plotting
maskcheck_filename = 'maskcheck.png'
display = plotting.plot_anat(epi,
display_mode='ortho',
draw_cross=False,
title='brainmask coverage')
display.add_contours(brainmask, levels=[.5], colors='r')
display.savefig(maskcheck_filename)
display.close()
maskcheck_file = os.path.abspath(maskcheck_filename)
return (maskcheck_file)
def main(argv=None):
import os
import sys
from nipype import config
config.enable_debug_mode()
#-------------------------------- argument parser
import argparse
argParser = argparse.ArgumentParser( description="""****************************
similarity computation between two labels
""")
# workup arguments
argParser.add_argument('--labelMapFilename1',
help="""a filename that will be compared to. """,
dest='labelMapFilename1', required=False)
argParser.add_argument('--labelMapFilename2',
help="""a filename that will be compared to. """,
dest='labelMapFilename2', required=False)
argParser.add_argument('--outputCSVFilename',
help="""a filename that will store comparative results to. """,
dest='outputCSVFilename', required=False)
argParser.add_argument('--doUnitTest', action='store_true',
help="""Do unit test if given""",
dest='doUnitTest', required=False)
args = argParser.parse_args()
action=False
if args.doUnitTest :
unitTest()
action=True
if args.labelMapFilename1 or args.labelMapFilename2:
print os.path.abspath( args.labelMapFilename1 )
print os.path.abspath( args.labelMapFilename2 )
print os.path.abspath( args.outputCSVFilename )
computeSimilarity( os.path.abspath( args.labelMapFilename1 ),
os.path.abspath( args.labelMapFilename2 ),
os.path.abspath( args.outputCSVFilename ) )
action=True
if not action:
print """ ***
def combine_fd(fd_list):
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from os.path import abspath
from numpy import asarray, savetxt
motion = open(fd_list[0]).read().splitlines()
if len(fd_list)>1:
for file in fd_list[1:]:
temp = open(file).read().splitlines()
motion = motion+temp
motion = asarray(motion).astype(float)
filename = 'FD_full.txt'
savetxt(filename,motion)
combined_fd = abspath(filename)
return(combined_fd)
def setup(argv):
print "Configuring environment..."
import os, os.path
from utilities.configFileParser import resolveDataSinkOption, parseFile
from utilities.pathHandling import validatePath
from utilities import misc
environment, experiment, pipeline, cluster = parseFile(argv["--ExperimentConfig"], argv["--pe"])
pipeline['ds_overwrite'] = resolveDataSinkOption(argv, pipeline)
if cluster is None:
assert argv["--wf_template_runner"] in misc.WFRUN, \
"wf_template_runner options for clusters can only be given when the configuration file's CLUSTER option == True"
os.environ['NSLOTS'] = str(misc.get_cpus(argv["--wf_template_runner"]))
else:
load_modules(cluster['modules']) # Load modules if not already done ## MODS PATH
# print os.environ['LOADEDMODULES']
if environment['virtualenv_dir']: ## MODS PATH
activate_this = validatePath(os.path.join(environment['virtualenv_dir'], 'bin', 'activate_this.py'), False, False)
execfile(activate_this, dict(__file__=activate_this))
utilities_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'utilities')
configure_env = validatePath(os.path.join(utilities_path, 'configure_env.py'), False, False)
# Add the AutoWorkup directory to the PYTHONPATH every time - REQUIRED FOR CLUSTER DISPATCHING
environment['env']['PYTHONPATH'] = environment['env']['PYTHONPATH'] + ":" + os.path.dirname(__file__)
execfile(configure_env, dict(__file__=__file__,
append_os_path=environment['env']['PATH'],
append_sys_path=environment['env']['PYTHONPATH'])
) ## MODS PATH
from nipype import config
config.enable_debug_mode()
from utilities.package_check import verify_packages
verify_packages()
if 'FREESURFER' in experiment['components']: ## FREESURFER MODS
configure_FS = validatePath(os.path.join(utilities_path, 'utilities', 'configure_FS.py'), False, False)
execfile(configure_FS, dict(FS_VARS=misc.FS_VARS, env=environment['env']))
print "FREESURFER needs to check for sane environment here!" # TODO: raise warning, write method, what???
for key, value in environment['env'].items():
if key in ['PATH', 'PYTHONPATH'] + misc.FS_VARS:
pass
else:
os.environ[key] = value # Do not use os.putenv (see Python documentation)
return environment, experiment, pipeline, cluster
def bandpass_filter(in_file, lowpass, highpass, TR):
import numpy as np
import nibabel as nb
from os import path
from nipype.interfaces.afni.preprocess import Bandpass
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
out_file = 'func_filtered.nii'
bp = Bandpass()
bp.inputs.highpass = highpass
bp.inputs.lowpass = lowpass
bp.inputs.in_file = in_file
bp.inputs.tr = TR
bp.inputs.out_file = out_file
bp.inputs.outputtype = 'NIFTI'
bp.run()
out_file = path.abspath(out_file)
return (out_file)
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 summarize_motion(motion_df_file, motion_file, vols_to_censor, TR):
from nipype import config, logging
config.enable_debug_mode()
logging.update_logging(config)
from os.path import dirname, basename
from numpy import asarray, mean, insert, zeros, sort
from pandas import DataFrame, Series, read_csv
motion_df = read_csv(motion_df_file, index_col=0)
motion = asarray(open(motion_file).read().splitlines()).astype(float)
censvols = asarray(open(vols_to_censor).read().splitlines()).astype(int)
sec_not_censored = (len(motion)-len(censvols))*TR
if censvols[0]>0:
periods_not_censored = insert(censvols,0,0)
else:
periods_not_censored = censvols
if periods_not_censored[-1]<len(motion):
periods_not_censored = insert(periods_not_censored,len(periods_not_censored),len(motion))
lengths = zeros(len(periods_not_censored)-1)
for a in range(0,len(lengths)):
lengths[a] = periods_not_censored[a+1] - periods_not_censored[a] - 1
lengths = lengths*TR
# sort lengths in descending order
lengths = sort(lengths)[::-1]
fp = dirname(motion_file)
subject = basename(fp)
motion_df.loc[subject] = [mean(motion),max(motion),len(censvols),len(motion),sec_not_censored,lengths]
motion_df.to_csv(motion_df_file)
return()
def MasterProcessingController(argv=None):
import argparse
import configparser
import csv
import string
if argv == None:
argv = sys.argv
# Create and parse input arguments
parser = argparse.ArgumentParser(description='Runs a mini version of BRAINSAutoWorkup')
group = parser.add_argument_group('Required')
group.add_argument('-pe', action="store", dest='processingEnvironment', required=True,
help='The name of the processing environment to use from the config file')
group.add_argument('-wfrun', action="store", dest='wfrun', required=True,
help='The name of the workflow running plugin to use')
group.add_argument('-subject', action="store", dest='subject', required=True,
help='The name of the subject to process')
group.add_argument('-ExperimentConfig', action="store", dest='ExperimentConfig', required=True,
help='The path to the file that describes the entire experiment')
parser.add_argument('-rewrite_datasinks', action='store_true', default=False,
help='Use if the datasinks should be forced rerun.\nDefault: value in configuration file')
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
args = parser.parse_args()
config = configparser.ConfigParser(allow_no_value=True)
config.read(args.ExperimentConfig)
# Pipeline-specific information
GLOBAL_DATA_SINK_REWRITE = setDataSinkRewriteValue(args.rewrite_datasinks,
config.getboolean('NIPYPE', 'GLOBAL_DATA_SINK_REWRITE'))
experiment = get_experiment_settings(config)
# Platform specific information
environment = get_environment_settings(config)
if environment['cluster']:
cluster = get_cluster_settings(config)
sys.path = environment('PYTHONPATH')
os.environ['PATH'] = ':'.join(environment['PATH'])
# Virtualenv
if not environment['virtualenv_dir'] is None:
print("Loading virtualenv_dir...")
execfile(environment['virtualenv_dir'], dict(__file__=environment['virtualenv_dir']))
###### Now ensure that all the required packages can be read in from this custom path
# \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# print sys.path
## Check to ensure that SimpleITK can be found
import SimpleITK as sitk
from nipype import config # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
# config.enable_provenance()
##############################################################################
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory
from nipype.interfaces.base import traits, isdefined, BaseInterface
from nipype.interfaces.utility import Merge, Split, Function, Rename, IdentityInterface
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
from nipype.interfaces.freesurfer import ReconAll
from nipype.utils.misc import package_check
# package_check('nipype', '5.4', 'tutorial1') ## HACK: Check nipype version
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
try:
verify_empty_freesurfer_env()
except EnvironmentError:
raise
# Define platform specific output write paths
if not os.path.exists(experiment['output_cache']):
os.makedirs(experiment['output_cache'])
if not os.path.exists(experiment['output_results']):
os.makedirs(experiment['output_results'])
if 'input_results' in list(experiment.keys()):
assert os.path.exists(
experiment['input_results']), "The previous experiment directory does not exist: {0}".format(
experiment['input_results'])
# \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# Define platform specific output write paths
mountPrefix = expConfig.get(input_arguments.processingEnvironment, 'MOUNTPREFIX')
BASEOUTPUTDIR = expConfig.get(input_arguments.processingEnvironment, 'BASEOUTPUTDIR')
ExperimentBaseDirectoryPrefix = os.path.realpath(os.path.join(BASEOUTPUTDIR, ExperimentName))
ExperimentBaseDirectoryCache = ExperimentBaseDirectoryPrefix + "_CACHE"
ExperimentBaseDirectoryResults = ExperimentBaseDirectoryPrefix + "_Results"
if not os.path.exists(ExperimentBaseDirectoryCache):
os.makedirs(ExperimentBaseDirectoryCache)
if not os.path.exists(ExperimentBaseDirectoryResults):
os.makedirs(ExperimentBaseDirectoryResults)
if not PreviousExperimentName is None:
PreviousBaseDirectoryPrefix = os.path.realpath(os.path.join(BASEOUTPUTDIR, PreviousExperimentName))
PreviousBaseDirectoryResults = PreviousBaseDirectoryPrefix + "_Results"
assert os.path.exists(
PreviousBaseDirectoryResults), "The previous experiment directory does not exist: {0}".format(
PreviousBaseDirectoryResults)
else:
PreviousBaseDirectoryResults = None
# Define workup common reference data sets
# The ATLAS needs to be copied to the ExperimentBaseDirectoryPrefix
# The ATLAS pathing must stay constant
ATLASPATH = expConfig.get(input_arguments.processingEnvironment, 'ATLASPATH')
if not os.path.exists(ATLASPATH):
print("ERROR: Invalid Path for Atlas: {0}".format(ATLASPATH))
sys.exit(-1)
CACHE_ATLASPATH = os.path.realpath(os.path.join(ExperimentBaseDirectoryCache, 'Atlas'))
from distutils.dir_util import copy_tree
if not os.path.exists(CACHE_ATLASPATH):
print("Copying a reference of the atlas to the experiment cache directory:\n from: {0}\n to: {1}".format(
ATLASPATH, CACHE_ATLASPATH))
copy_tree(ATLASPATH, CACHE_ATLASPATH, preserve_mode=1, preserve_times=1)
## Now generate the xml file with the correct pathing
file_replace(os.path.join(ATLASPATH, 'ExtendedAtlasDefinition.xml.in'),
os.path.join(CACHE_ATLASPATH, 'ExtendedAtlasDefinition.xml'), "@ATLAS_INSTALL_DIRECTORY@",
CACHE_ATLASPATH)
else:
print("Atlas already exists in experiment cache directory: {0}".format(CACHE_ATLASPATH))
## Set custom environmental variables so that subproceses work properly (i.e. for FreeSurfer)
CUSTOM_ENVIRONMENT = eval(environment['misc'])
# print CUSTOM_ENVIRONMENT
for key, value in list(CUSTOM_ENVIRONMENT.items()):
# print "SETTING: ", key, value
os.putenv(key, value)
os.environ[key] = value
# print os.environ
# sys.exit(-1)
WORKFLOW_COMPONENTS = experiment['components']
if 'FREESURFER' in WORKFLOW_COMPONENTS:
check_freesurfer_environment()
cluster = setup_cpu(args.wfrun, config) # None unless wfrun is 'helium*' or 'ipl_OSX', then dict()
print("Configuring Pipeline")
## Ensure that entire db is built and cached before parallel section starts.
_ignoreme = OpenSubjectDatabase(experiment['output_cache'], ["all"], environment['prefix'],
environment['subject_data_file'])
to_do_subjects = args.subject.split(',')
if to_do_subjects[0] == "all":
to_do_subjects = _ignoreme.getAllSubjects()
_ignoreme = None
## Create the shell wrapper script for ensuring that all jobs running on remote hosts from SGE
# have the same environment as the job submission host.
JOB_SCRIPT = get_global_sge_script(sys.path, os.environ['PATH'], CUSTOM_ENVIRONMENT, MODULES)
print(JOB_SCRIPT)
# Randomly shuffle to_do_subjects to get max
import random
random.shuffle(to_do_subjects)
## Make a list of all the arguments to be processed
sp_args_list = list()
start_time = time.time()
subj_index = 1
for subjectid in to_do_subjects:
delay = 2.5 * subj_index
subj_index += 1
print("START DELAY: {0}".format(delay))
sp_args = (CACHE_ATLASPATH, CLUSTER_QUEUE, CLUSTER_QUEUE_LONG, QSTAT_IMMEDIATE_EXE, QSTAT_CACHED_EXE,
experiment['output_cache'], experiment['output_results'], environment['subject_data_file'],
GLOBAL_DATA_SINK_REWRITE, JOB_SCRIPT, WORKFLOW_COMPONENTS, args,
mountPrefix, start_time + delay, subjectid, PreviousBaseDirectoryResult)
sp_args_list.append(sp_args)
if 'local' in args.wfrun:
print("RUNNING WITHOUT POOL BUILDING")
for sp_args in sp_args_list:
DoSingleSubjectProcessing(sp_args)
else:
## Make a pool of workers to submit simultaneously
from multiprocessing import Pool
myPool = Pool(processes=64, maxtasksperchild=1)
all_results = myPool.map_async(DoSingleSubjectProcessing, sp_args_list).get(1e100)
for indx in range(0, len(sp_args_list)):
if all_results[indx] == False:
print("FAILED for {0}".format(sp_args_list[indx][-1]))
print("THIS RUN OF BAW FOR SUBJS {0} HAS COMPLETED".format(to_do_subjects))
return 0
def main(argv=None):
import os
import sys
import nipype.pipeline.engine as pe
from nipype.interfaces.utility import Function
import ConfigurationParser
from nipype import config
config.enable_debug_mode()
workflow = pe.Workflow(name='crossValidation')
workflow.base_dir = '.'
#-------------------------------- argument parser
import argparse
argParser = argparse.ArgumentParser( description="""****************************
10-cross validation command line argument parser
""")
# workup arguments
argWfGrp = argParser.add_argument_group( 'argWfGrp', """****************************
auto workflow arguments for cross validation
""")
argWfGrp.add_argument('--crossValidationConfigurationFilename',
help="""configurationFilename
Configuration file name with FULL PATH""",
dest='crossValidationConfigurationFilename', required=True)
argWfGrp.add_argument( '--baseDir', help="""baseDir
""",
dest='baseDir', required=False, default=".")
argWfGrp.add_argument( '--runOption', help="""runOption [local/cluster]
""",
dest='runOption', required=False, default="local")
argWfGrp.add_argument( '--PythonBinDir', help="""PythonBinDir [local/cluster]
""",
dest='PythonBinDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsSrcDir', help="""BRAINSToolsSrcDir [local/cluster]
""",
dest='BRAINSToolsSrcDir', required=False, default="NA")
argWfGrp.add_argument( '--BRAINSToolsBuildDir', help="""BRAINSToolsBuildDir [local/cluster]
""",
dest='BRAINSToolsBuildDir', required=False, default="NA")
# test arguments
argTestGrp = argParser.add_argument_group( 'argTestGrp', """****************************
arguments for testing
""")
argTestGrp.add_argument('--unitTest', action='store_true',
dest='unitTest', help="""****************************
List of test function name
""")
args = argParser.parse_args()
#--------------------------------
if not args.unitTest:
crossValidationWorkUp(args.crossValidationConfigurationFilename,
args.baseDir,
args.runOption,
args.PythonBinDir,
args.BRAINSToolsSrcDir,
args.BRAINSToolsBuildDir)
#--------------------------------
if args.unitTest:
testElementPerSubject = [3, 4, 5]
getStartAndEndIndex(0, testElementPerSubject)
getStartAndEndIndex(1, testElementPerSubject)
getStartAndEndIndex(2, testElementPerSubject)
featureDict = {'GadSG': 'testGadFeatureList.csv',
't2': 't2FeatureList.csv'}
sessionList = ["s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "s9", "s10", "s11", "s12"]
getRandomizedSessionOrder(sessionList)
myTag = getTags(sessionList,
2,
testElementPerSubject)
featureFilenameDict = {'f1': 'f1.csv', 'f2': 'f2.csv'}
configFilename, mainFilenameDict, featureFilenameDict = generateNewFilenames(3,
list(featureFilenameDict.keys()),
"outputPrefix")
import ConfigurationParser
m_configurationMap = ConfigurationParser.ConfigurationSectionMap(args.crossValidationConfigurationFilename)
listFiles = m_configurationMap['ListFiles']
mainListFilename = listFiles['subjectListFilename'.lower()]
sessionDict = readListFileBySessionID(mainListFilename)
myTag = getTags(list(sessionDict.keys()),
2,
listFiles['numberOfElementInSubset'.lower()])
writeListFile(sessionDict,
mainFilenameDict,
myTag)
def pipeline(args):
if args['debug']:
config.enable_debug_mode()
config.update_config({'logging': {'log_directory':makeSupportDir(args['name'], "logs")}})
logging.update_logging(config)
# CONSTANTS
sessionID = args['session']
outputType = args['format'].upper()
fOutputType = args['freesurfer']
preprocessOn = args['preprocess']
maskGM = args['maskgm']
maskWholeBrain = args['maskwb']
maskWhiteMatterFromSeeds = args['maskseeds']
# print args['name']
t1_experiment = "20141001_PREDICTHD_long_Results" #"20130729_PREDICT_Results"
atlasFile = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_t1.nii.gz"))
wholeBrainFile = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_brain.nii.gz"))
atlasLabel = os.path.abspath(os.path.join(os.path.dirname(__file__), "ReferenceAtlas", "template_nac_labels.nii.gz"))
resampleResolution = (2.0, 2.0, 2.0)
downsampledfilename = 'downsampled_atlas.nii.gz'
master = pipe.Workflow(name=args['name'] + "_CACHE")
master.base_dir = os.path.abspath("/Shared/sinapse/CACHE")
sessions = pipe.Node(interface=IdentityInterface(fields=['session_id']), name='sessionIDs')
sessions.iterables = ('session_id', sessionID)
downsampleAtlas = pipe.Node(interface=Function(function=resampleImage,
input_names=['inputVolume', 'outputVolume', 'resolution'],
output_names=['outputVolume']),
name="downsampleAtlas")
downsampleAtlas.inputs.inputVolume = atlasFile
downsampleAtlas.inputs.outputVolume = downsampledfilename
downsampleAtlas.inputs.resolution = [int(x) for x in resampleResolution]
# HACK: Remove node from pipeline until Nipype/AFNI file copy issue is resolved
# fmri_DataSink = pipe.Node(interface=DataSink(), name="fmri_DataSink")
# fmri_DataSink.overwrite = REWRITE_DATASINKS
# Output to: /Shared/paulsen/Experiments/YYYYMMDD_<experiment>_Results/fmri
# fmri_DataSink.inputs.base_directory = os.path.join(master.base_dir, RESULTS_DIR, 'fmri')
# fmri_DataSink.inputs.substitutions = [('to_3D_out+orig', 'to3D')]
# fmri_DataSink.inputs.parameterization = False
#
# master.connect([(sessions, fmri_DataSink, [('session_id', 'container')])])
# END HACK
registration = registrationWorkflow.workflow(t1_experiment, outputType, name="registration_wkfl")
master.connect([(sessions, registration, [('session_id', "inputs.session_id")])])
detrend = afninodes.detrendnode(outputType, 'afni3Ddetrend')
# define grabber
site = "*"
subject = "*"
if preprocessOn:
grabber = dataio.iowaGrabber(t1_experiment, site, subject, maskGM, maskWholeBrain)
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')])])
# Why isn't preprocessWorkflow.workflow() used instead? It would avoid most of the nuisance connections here...
preprocessing = preprocessWorkflow.prepWorkflow(skipCount=6, outputType=outputType)
name = args.pop('name') # HACK: prevent name conflict with nuisance workflow
nuisance = nuisanceWorkflow.workflow(outputType=outputType, **args)
args['name'] = name # END HACK
master.connect([(grabber, preprocessing, [('fmri_dicom_dir', 'to_3D.infolder'),
('fmri_dicom_dir', 'formatFMRINode.dicomDirectory')]),
(grabber, nuisance, [('whmFile', 'wm.warpWMtoFMRI.input_image')]),
(preprocessing, registration, [('merge.out_file', 'inputs.fmri'), # 7
('automask.out_file', 'tstat.mask_file')]), # *optional*
(registration, nuisance, [('outputs.fmri_reference', 'csf.warpCSFtoFMRI.reference_image'), # CSF
('outputs.nac2fmri_list', 'csf.warpCSFtoFMRI.transforms'),
('outputs.fmri_reference', 'wm.warpWMtoFMRI.reference_image'), # WM
('outputs.t12fmri_list', 'wm.warpWMtoFMRI.transforms')]),
])
warpCSFtoFMRInode = nuisance.get_node('csf').get_node('warpCSFtoFMRI')
warpCSFtoFMRInode.inputs.input_image = atlasFile
if maskGM:
master.connect([(grabber, nuisance, [('gryFile', 'gm.warpGMtoFMRI.input_image')]),
(registration, nuisance, [('outputs.fmri_reference', 'gm.warpGMtoFMRI.reference_image'),
('outputs.t12fmri_list', 'gm.warpGMtoFMRI.transforms')]),
(preprocessing, nuisance, [('calc.out_file', 'gm.afni3DmaskAve_grm.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])])
elif maskWholeBrain:
master.connect([(registration, nuisance, [('outputs.fmri_reference', 'wb.warpBraintoFMRI.reference_image'),
('outputs.nac2fmri_list', 'wb.warpBraintoFMRI.transforms')]),
(preprocessing, nuisance, [('calc.out_file', 'wb.afni3DmaskAve_whole.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])])
warpBraintoFMRInode = nuisance.get_node('wb').get_node('warpBraintoFMRI')
warpBraintoFMRInode.inputs.input_image= wholeBrainFile
else:
master.connect([(preprocessing, nuisance, [('volreg.oned_file', 'afni3Ddeconvolve.stim_file_3')])])
master.connect([(preprocessing, nuisance, [('calc.out_file', 'wm.afni3DmaskAve_wm.in_file'),
('calc.out_file', 'csf.afni3DmaskAve_csf.in_file'),
('calc.out_file', 'afni3Ddeconvolve.in_file')]),
(nuisance, detrend, [('afni3Ddeconvolve.out_errts', 'in_file')])]) # 13
else:
cleveland_grabber = dataio.clevelandGrabber()
grabber = dataio.autoworkupGrabber(t1_experiment, site, subject)
converter = pipe.Node(interface=Copy(), name='converter') # Convert ANALYZE to AFNI
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')]),
(sessions, cleveland_grabber, [('session_id', 'session_id')]),
(cleveland_grabber, converter, [('fmriHdr', 'in_file')]),
(converter, registration, [('out_file', 'inputs.fmri')]),
(converter, detrend, [('out_file', 'in_file')]), # in fMRI_space
])
t1_wf = registrationWorkflow.t1Workflow()
babc_wf = registrationWorkflow.babcWorkflow()
# HACK: No EPI
# epi_wf = registrationWorkflow.epiWorkflow()
lb_wf = registrationWorkflow.labelWorkflow()
seed_wf = registrationWorkflow.seedWorkflow()
bandpass = afninodes.fouriernode(outputType, 'fourier') # Fourier is the last NIFTI file format in the AFNI pipeline
master.connect([(detrend, bandpass, [('out_file', 'in_file')]), # Per Dawei, bandpass after running 3dDetrend
(grabber, t1_wf, [('t1_File', 'warpT1toFMRI.input_image')]),
(registration, t1_wf, [('outputs.fmri_reference', 'warpT1toFMRI.reference_image'), # T1
('outputs.t12fmri_list', 'warpT1toFMRI.transforms')]),
(grabber, babc_wf, [('csfFile', 'warpBABCtoFMRI.input_image')]),
(registration, babc_wf, [('outputs.fmri_reference', 'warpBABCtoFMRI.reference_image'), # Labels
('outputs.t12fmri_list', 'warpBABCtoFMRI.transforms')]),
# HACK: No EPI
# (downsampleAtlas, epi_wf, [('outputVolume', 'warpEPItoNAC.reference_image')]),
# (registration, epi_wf, [('outputs.fmri2nac_list', 'warpEPItoNAC.transforms')]),
# (bandpass, epi_wf, [('out_file', 'warpEPItoNAC.input_image')]),
# END HACK
(downsampleAtlas, lb_wf, [('outputVolume', 'warpLabeltoNAC.reference_image')]),
(registration, lb_wf, [('outputs.fmri2nac_list', 'warpLabeltoNAC.transforms')]),
(t1_wf, seed_wf, [('warpT1toFMRI.output_image', 'warpSeedtoFMRI.reference_image')]),
(registration, seed_wf, [('outputs.nac2fmri_list', 'warpSeedtoFMRI.transforms')]),
])
renameMasks = pipe.Node(interface=Rename(format_string='%(label)s_mask'), name='renameMasksAtlas')
renameMasks.inputs.keep_ext = True
atlas_DataSink = dataio.atlasSink(base_directory=master.base_dir, **args)
master.connect([(renameMasks, atlas_DataSink, [('out_file', 'Atlas')]),
(downsampleAtlas, atlas_DataSink, [('outputVolume', 'Atlas.@resampled')]),
])
renameMasks2 = pipe.Node(interface=Rename(format_string='%(session)s_%(label)s_mask'), name='renameMasksFMRI')
renameMasks2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameMasks2, 'session')
clipSeedWithVentriclesNode = pipe.Node(interface=Function(function=clipSeedWithVentricles,
input_names=['seed', 'label', 'outfile'],
output_names=['clipped_seed_fn']),
name='clipSeedWithVentriclesNode')
clipSeedWithVentriclesNode.inputs.outfile = "clipped_seed.nii.gz"
master.connect(seed_wf, 'warpSeedtoFMRI.output_image', clipSeedWithVentriclesNode, 'seed')
master.connect(babc_wf, 'warpBABCtoFMRI.output_image', clipSeedWithVentriclesNode, 'label')
if not maskWhiteMatterFromSeeds:
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn', renameMasks2, 'in_file')
else:
clipSeedWithWhiteMatterNode = pipe.Node(interface=Function(function=clipSeedWithWhiteMatter,
input_names=['seed', 'mask', 'outfile'],
output_names=['outfile']),
name='clipSeedWithWhiteMatterNode')
clipSeedWithWhiteMatterNode.inputs.outfile = 'clipped_wm_seed.nii.gz'
master.connect(babc_wf, 'warpBABCtoFMRI.output_image', clipSeedWithWhiteMatterNode, 'mask')
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn', clipSeedWithWhiteMatterNode, 'seed')
master.connect(clipSeedWithWhiteMatterNode, 'outfile', renameMasks2, 'in_file')
# Labels are iterated over, so we need a seperate datasink to avoid overwriting any preprocessing
# results when the labels are iterated (e.g. To3d output)
# Write out to: /Shared/sinapse/CACHE/YYYYMMDD_<experiment>_Results/<SESSION>
fmri_label_DataSink = dataio.fmriSink(master.base_dir, **args)
master.connect(sessions, 'session_id', fmri_label_DataSink, 'container')
master.connect(renameMasks2, 'out_file', fmri_label_DataSink, 'masks')
master.connect(bandpass,'out_file', fmri_label_DataSink, 'masks.@bandpass')
roiMedian = afninodes.maskavenode('AFNI_1D', 'afni_roiMedian', '-mrange 1 1')
master.connect(renameMasks2, 'out_file', roiMedian, 'mask')
master.connect(bandpass, 'out_file', roiMedian, 'in_file')
correlate = afninodes.fimnode('Correlation', 'afni_correlate')
master.connect(roiMedian, 'out_file', correlate, 'ideal_file')
master.connect(bandpass, 'out_file', correlate, 'in_file')
regionLogCalc = afninodes.logcalcnode(outputType, 'afni_regionLogCalc')
master.connect(correlate, 'out_file', regionLogCalc, 'in_file_a')
renameZscore = pipe.Node(interface=Rename(format_string="%(session)s_%(label)s_zscore"), name='renameZscore')
renameZscore.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore, 'session')
master.connect(regionLogCalc, 'out_file', renameZscore, 'in_file')
master.connect(renameZscore, 'out_file', fmri_label_DataSink, 'zscores')
master.connect(t1_wf, 'warpT1toFMRI.output_image', fmri_label_DataSink, 'zscores.@t1Underlay')
# Move z values back into NAC atlas space
# master.connect(downsampleAtlas, 'outputVolume', lb_wf, 'warpLabeltoNAC.reference_image')
master.connect(regionLogCalc, 'out_file', lb_wf, 'warpLabeltoNAC.input_image')
renameZscore2 = pipe.Node(interface=Rename(format_string="%(session)s_%(label)s_result"), name='renameZscore2')
renameZscore2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore2, 'session')
master.connect(lb_wf, 'warpLabeltoNAC.output_image', renameZscore2, 'in_file')
master.connect(renameZscore2, 'out_file', atlas_DataSink, 'Atlas.@zscore')
# Connect seed subworkflow
seedSubflow = seedWorkflow.workflow(args['seeds'], outputType='NIFTI_GZ', name='seed_wkfl')
master.connect([(downsampleAtlas, seedSubflow, [('outputVolume', 'afni3Dcalc_seeds.in_file_a')]),
(seedSubflow, renameMasks, [('afni3Dcalc_seeds.out_file', 'in_file'),
('selectLabel.out', 'label')]),
(seedSubflow, renameMasks2, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore2, [('selectLabel.out', 'label')]),
(seedSubflow, seed_wf, [('afni3Dcalc_seeds.out_file', 'warpSeedtoFMRI.input_image')])
])
imageDir = makeSupportDir(args['name'], "images")
if args['plot']:
registration.write_graph(dotfilename=os.path.join(imageDir, 'register.dot'), graph2use='orig', format='png',
simple_form=False)
if preprocessOn:
preprocessing.write_graph(dotfilename=os.path.join(imageDir, 'preprocess.dot'), graph2use='orig', format='png',
simple_form=False)
nuisance.write_graph(dotfilename=os.path.join(imageDir, 'nuisance.dot'), graph2use='orig', format='png',
simple_form=False)
seedSubflow.write_graph(dotfilename=os.path.join(imageDir, 'seed.dot'), graph2use='orig', format='png',
simple_form=False)
master.write_graph(dotfilename=os.path.join(imageDir, 'master.dot'), graph2use="orig", format='png', simple_form=False)
elif args['debug']:
try:
master.run(updatehash=True)
# Run restingState on the all threads
# Setup environment for CPU load balancing of ITK based programs.
# --------
# import multiprocessing
# total_CPUS = 10 # multiprocessing.cpu_count()
# master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
# --------
# Run restingState on the local cluster
# master.run(plugin='SGE', plugin_args={'template': os.path.join(os.getcwd(), 'ENV/bin/activate'),
# 'qsub_args': '-S /bin/bash -cwd'}) #, updatehash=True)
except:
pass
master.name = "master" # HACK: Bug in Graphviz for nodes beginning with numbers
master.write_graph(dotfilename=os.path.join(imageDir, 'debug_hier.dot'), graph2use="colored", format='png')
master.write_graph(dotfilename=os.path.join(imageDir, 'debug_orig.dot'), graph2use="flat", format='png')
else:
import multiprocessing
total_CPUS = multiprocessing.cpu_count()
master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
return 0
def crossValidationWorkUp(crossValidationConfigurationFilename,
baseDir,
runOption,
PythonBinDir,
BRAINSToolsSrcDir,
BRAINSToolsBuildDir):
print("""****************************
crossValidationWorkUp
""")
from collections import OrderedDict # Need OrderedDict internally to ensure consistent ordering
from nipype import config
config.enable_debug_mode()
import crossValidation as this
import ConfigurationParser
myConfigurationMap = ConfigurationParser.ConfigurationSectionMap(
crossValidationConfigurationFilename)
import nipype.pipeline.engine as pe
from nipype.interfaces.utility import Function
import ast
print( """ before
createeachvalidationunitnd
""")
createConfigurationFiles = pe.Node(name="createConfigurationFiles",
interface=Function(
input_names=['inputConfigurationFilename',
'outputConfigurationFilenamePrefix'],
output_names=['outputConfigFilenameDict'],
function=this.createConfigurationFileForCrossValidationUnitTest)
)
preprocessing = pe.Workflow(name='Preprocessing')
preprocessing.base_dir = baseDir + "/PreprocessingDir"
createConfigurationFiles.inputs.inputConfigurationFilename = crossValidationConfigurationFilename
createConfigurationFiles.inputs.outputConfigurationFilenamePrefix = 'createConfigurationFiles'
extractConfigurationFileListND = pe.Node(name="extractConfigurationFileListND",
interface=Function(
input_names=['configurationFiledict'],
output_names=['configurationFileList'],
function=this.extractConfigFile)
)
preprocessing.connect(createConfigurationFiles, 'outputConfigFilenameDict',
extractConfigurationFileListND, 'configurationFiledict')
preprocessing.run()
#------------------------------------------------------------------------------------
# Data graber for outputs
#
import nipype.interfaces.io as nio
dg = nio.DataGrabber()
dg.inputs.base_directory = baseDir + "/PreprocessingDir/Preprocessing/createConfigurationFiles/"
dg.inputs.template = "*config"
mainConfigFiles = dg.run()
print((mainConfigFiles.outputs.outfiles))
print((mainConfigFiles.outputs.outfiles))
print((mainConfigFiles.outputs.outfiles))
print((mainConfigFiles.outputs.outfiles))
#------------------------------------------------------------------------------------
workflow = pe.Workflow(name='crossValidationWF')
workflow.base_dir = baseDir
#------------------------------------------------------------------------------------
# Generate Probability Map
#
Options = myConfigurationMap['Options']
roiDict = Options['roiBooleanCreator'.lower()]
#-------------------------------- probMapFilenameGenerator is dummy node
# to create proper probability file location for nipype
#
print("""************************
probMapFilenameGenerator
""")
probMapFilenameGenerator = pe.Node(name="probMapFilenameGenerator",
interface=Function(
input_names=['roiList'],
output_names=['probabilityMapFilename'],
function=this.getProbabilityMapFilename)
)
print(roiDict)
probMapFilenameGenerator.inputs.roiList = list(roiDict.keys())
print("""************************
probabilityMapGeneratorND
""")
#
#-------------------------------- start from generate probability
#
probabilityMapGeneratorND = pe.Node(name="probabilityMapGeneratorND",
interface=Function(
input_names=['configurationFilename',
'probabilityMapDict',
'gaussianSigma',
'outputXmlFilename'],
output_names=['probabilityMapDict',
'outputXmlFilename',
'outputConfigurationFilename'],
function=ConfigurationParser.BRAINSCutGenerateProbabilityMap)
)
probabilityMapGeneratorND.inputs.outputXmlFilename = 'netConfiguration.xml'
gaussianSigmaParam = ast.literal_eval(Options['gaussianSigma'.lower()])
print (gaussianSigmaParam)
probabilityMapGeneratorND.iterables = ('configurationFilename', mainConfigFiles.outputs.outfiles)
probabilityMapGeneratorND.inputs.gaussianSigma = gaussianSigmaParam
workflow.connect(probMapFilenameGenerator, 'probabilityMapFilename',
probabilityMapGeneratorND, 'probabilityMapDict')
#
#-------------------------------- create vectors for each ROI
#
print("""************************
configFileND
""")
configFileND = pe.Node(name="configFileND",
interface=Function(
input_names=['originalFilename',
'editedFilenamePrefix'],
output_names=['editedFilenames'],
function=ConfigurationParser.ConfigurationFileEditor)
)
configFileND.inputs.editedFilenamePrefix = 'ROI'
workflow.connect(probabilityMapGeneratorND, 'outputConfigurationFilename',
configFileND, 'originalFilename')
vectorCreatorND = pe.MapNode(name="vectorCreatorND",
interface=Function(
input_names=['configurationFilename',
'probabilityMapDict',
'normalization',
'outputXmlFilename',
'outputVectorFilename'],
output_names=['outputVectorFilename',
'outputVectorHdrFilename',
'outputNormalization',
'outputXmlFilename'],
function=ConfigurationParser.BRAINSCutCreateVector),
iterfield=['configurationFilename']
)
vectorCreatorND.inputs.outputVectorFilename = 'oneROIVectorFile.txt'
vectorCreatorND.inputs.outputXmlFilename = 'oneROICreateVectorNetConfiguration.xml'
normalizationOption = Options['normalization'.lower()]
print(( """Normalization Option: {str}
""".format( str=normalizationOption ) ))
vectorCreatorND.iterables = ('normalization', normalizationOption)
#
#-------------------------------- workflow connections
#
workflow.connect(configFileND, 'editedFilenames',
vectorCreatorND, 'configurationFilename')
workflow.connect(probabilityMapGeneratorND, 'probabilityMapDict',
vectorCreatorND, 'probabilityMapDict')
#
#-------------------------------- balance and combine each ROI vectors
#
print("""************************
balanceND
""")
balaceND = pe.Node(name="balanceND",
interface=Function(
input_names=['inputVectorFilenames'],
output_names=['outputVectorFilenames',
'outputVectorHdrFilenames'],
function=ConfigurationParser.BalanceInputVectors)
)
workflow.connect(vectorCreatorND, 'outputVectorFilename',
balaceND, 'inputVectorFilenames')
combineND = pe.Node(name="combineND",
interface=Function(
input_names=['inputVectorFilenames',
'outputVectorFilename'],
output_names=['outputVectorFilename',
'outputVectorHdrFilename'],
function=ConfigurationParser.CombineInputVectors)
)
workflow.connect(balaceND, 'outputVectorFilenames',
combineND, 'inputVectorFilenames')
combineND.inputs.outputVectorFilename = 'allCombinedVector.txtANN'
#
#-------------------------------- train
#
print("""************************
trainND
""")
trainND = pe.Node(name="trainND",
interface=Function(
input_names=['configurationFilename',
'inputVectorFilename',
'outputModelFilenamePrefix',
'outputXmlFilename',
'methodParameter'],
output_names=['outputTrainedModelFilename',
'outputMethodParameter'],
function=ConfigurationParser.BRAINSCutTrainModel)
)
# methodParameter = { '--method': 'RandomForest',
# '--numberOfTrees': 60,
# '--randomTreeDepth ': 60 }
methodFromConfiguFile = Options['modelParameter'.lower()]
trainND.iterables = ('methodParameter', methodFromConfiguFile)
trainND.inputs.outputXmlFilename = 'trianNetConfiguration.xml'
trainND.inputs.outputModelFilenamePrefix = 'trainModelFile.txt'
workflow.connect(probabilityMapGeneratorND, 'outputConfigurationFilename',
trainND, 'configurationFilename')
workflow.connect(combineND, 'outputVectorFilename',
trainND, 'inputVectorFilename')
#
#-------------------------------- apply
#
applyND = pe.Node(name="applyND",
interface=Function(
input_names=['configurationFilename',
'probabilityMapDict',
'normalization',
'inputModelFilename',
'methodParameter',
'outputXmlFilename'
],
output_names=['outputLabelDict'],
function=ConfigurationParser.BRAINSCutApplyModel)
)
# methodParameter = { '--method': 'RandomForest',
# '--numberOfTrees': 60,
# '--randomTreeDepth ': 60 }
applyND.inputs.outputXmlFilename = 'applyConfiguration.xml'
workflow.connect(probabilityMapGeneratorND, 'outputConfigurationFilename',
applyND, 'configurationFilename')
workflow.connect(vectorCreatorND, 'outputNormalization',
applyND, 'normalization')
workflow.connect(probabilityMapGeneratorND, 'probabilityMapDict',
applyND, 'probabilityMapDict')
workflow.connect(trainND, 'outputTrainedModelFilename',
applyND, 'inputModelFilename')
workflow.connect(trainND, 'outputMethodParameter',
applyND, 'methodParameter')
#####################################################################################
# Data Sink
#
import os
LabelsDS = pe.Node(nio.DataSink(), name='LabelDS')
LabelsDS.inputs.base_directory = os.path.join(baseDir, "Result")
LabelsDS.inputs.regexp_substitutions = [('/_', '/'),
('configurationFilename.*_Test', 'Test'),
('_configuration.config/normalization_', '/'),
('methodParameter_--method', ''),
('RandomForest', 'RF/'),
('.--randomTreeDepth', 'TreeDepth'),
('.--numberOfTrees', '_TreeNumber'),
('ANNContinuousPrediction(?P<roi>.+)(?P<session>\d\d\d\d\d).nii.gz', r'\g<session>_\g<roi>_ANNContinuous.nii.gz')
]
# ANNContinuousPredictionl_accumben77478
workflow.connect([(applyND, LabelsDS,
[(('outputLabelDict', getDictionaryValues), 'Labels')])])
#####################################################################################
# analysis
#
#####################################################################################
# Running
#
if runOption == "cluster":
############################################
# Platform specific information
# Prepend the python search paths
pythonPath = BRAINSToolsSrcDir + "/BRAINSCut/BRAINSFeatureCreators/RobustStatisticComputations:" + BRAINSToolsSrcDir + "/AutoWorkup/:" + BRAINSToolsSrcDir + "/AutoWorkup/BRAINSTools/:" + BRAINSToolsBuildDir + "/SimpleITK-build/bin/" + \
BRAINSToolsBuildDir + "/SimpleITK-build/lib:" + PythonBinDir
binPath = BRAINSToolsBuildDir + "/bin:" + BRAINSToolsBuildDir + "/lib"
PYTHON_AUX_PATHS = pythonPath
PYTHON_AUX_PATHS = PYTHON_AUX_PATHS.split(':')
PYTHON_AUX_PATHS.extend(sys.path)
sys.path = PYTHON_AUX_PATHS
# print sys.path
import SimpleITK as sitk
# Prepend the shell environment search paths
PROGRAM_PATHS = binPath
PROGRAM_PATHS = PROGRAM_PATHS.split(':')
import os
PROGRAM_PATHS.extend(os.environ['PATH'].split(':'))
os.environ['PATH'] = ':'.join(PROGRAM_PATHS)
Cluster_Script = get_global_sge_script(PYTHON_AUX_PATHS,
PROGRAM_PATHS,
{}
)
workflow.run(plugin='SGE',
plugin_args=OrderedDict(template=Cluster_Script,
qsub_args="-S /bin/bash -pe smp 4-8 -o /dev/null "))
else:
print("""************************
run
""")
try:
workflow.write_graph(graph2use='flat')
except:
pass
workflow.run()
def MasterProcessingController(argv=None):
import argparse
import ConfigParser
import csv
import string
if argv == None:
argv = sys.argv
# Create and parse input arguments
parser = argparse.ArgumentParser(description='Runs a mini version of BRAINSAutoWorkup')
group = parser.add_argument_group('Required')
group.add_argument('-pe', action="store", dest='processingEnvironment', required=True,
help='The name of the processing environment to use from the config file')
group.add_argument('-wfrun', action="store", dest='wfrun', required=True,
help='The name of the workflow running plugin to use')
group.add_argument('-subject', action="store", dest='subject', required=True,
help='The name of the subject to process')
group.add_argument('-ExperimentConfig', action="store", dest='ExperimentConfig', required=True,
help='The path to the file that describes the entire experiment')
parser.add_argument('-rewrite_datasinks', action='store_true', default=False,
help='Use if the datasinks should be forced rerun.\nDefault: value in configuration file')
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
input_arguments = parser.parse_args()
expConfig = ConfigParser.ConfigParser()
expConfig.read(input_arguments.ExperimentConfig)
# Pipeline-specific information
GLOBAL_DATA_SINK_REWRITE_FROM_CONFIG = expConfig.getboolean('PIPELINE', 'GLOBAL_DATA_SINK_REWRITE')
GLOBAL_DATA_SINK_REWRITE = setDataSinkRewriteValue(input_arguments.rewrite_datasinks, GLOBAL_DATA_SINK_REWRITE_FROM_CONFIG)
# Experiment specific information
subject_data_file = expConfig.get('EXPERIMENT_DATA', 'SESSION_DB')
ExperimentName = expConfig.get('EXPERIMENT_DATA', 'EXPERIMENTNAME')
if expConfig.has_option('EXPERIMENT_DATA', 'PREVIOUSEXPERIMENTNAME'):
PreviousExperimentName = expConfig.get('EXPERIMENT_DATA', 'PREVIOUSEXPERIMENTNAME')
else:
PreviousExperimentName = None
# Platform specific information
# Prepend the python search paths
PYTHON_AUX_PATHS = expConfig.get(input_arguments.processingEnvironment, 'PYTHON_AUX_PATHS')
PYTHON_AUX_PATHS = PYTHON_AUX_PATHS.split(':')
PYTHON_AUX_PATHS.extend(sys.path)
sys.path = PYTHON_AUX_PATHS
#####################################################################################
# Prepend the shell environment search paths
PROGRAM_PATHS = expConfig.get(input_arguments.processingEnvironment, 'PROGRAM_PATHS')
PROGRAM_PATHS = PROGRAM_PATHS.split(':')
PROGRAM_PATHS.extend(os.environ['PATH'].split(':'))
PROGRAM_PATHS = [os.path.dirname(__file__)] + PROGRAM_PATHS
print "Adding directory {0} to PATH...".format(os.path.dirname(__file__))
os.environ['PATH'] = ':'.join(PROGRAM_PATHS)
######################################################################################
# Get virtualenv source file
if expConfig.has_option(input_arguments.processingEnvironment, 'VIRTUALENV'):
print "Loading virtualenv..."
VIRTUALENV = expConfig.get(input_arguments.processingEnvironment, 'VIRTUALENV')
activate_this = os.path.join(VIRTUALENV, 'bin', 'activate_this.py')
execfile(activate_this, dict(__file__=activate_this))
###### Now ensure that all the required packages can be read in from this custom path
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# print sys.path
from nipype import config # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
##############################################################################
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory
from nipype.interfaces.base import traits, isdefined, BaseInterface
from nipype.interfaces.utility import Merge, Split, Function, Rename, IdentityInterface
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
from nipype.interfaces.freesurfer import ReconAll
from nipype.utils.misc import package_check
# package_check('nipype', '5.4', 'tutorial1') ## HACK: Check nipype version
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
## Check to ensure that SimpleITK can be found
import SimpleITK as sitk
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
#####################################################################################
# FreeSurfer is extraordinarly finicky and is easily confused and incorrect.
# Force that all the FREESURFER env vars are set in subsequent scripts by
# ensuring that rough versions of these environmental variables are not
# set internal to this script.
prohibited_env_var_exists = False
for ENVVAR_TO_CHECK in ['FREESURFER_HOME', 'FSFAST_HOME', 'FSF_OUTPUT_FORMAT', 'SUBJECTS_DIR', 'MNI_DIR', 'FSL_DIR']:
if ENVVAR_TO_CHECK in os.environ:
prohibited_env_var_exists = True
print("ERROR: Environmental Variable {0}={1} exists. Please unset before continuing.".format(ENVVAR_TO_CHECK, os.environ[ENVVAR_TO_CHECK]))
if prohibited_env_var_exists:
sys.exit(-1)
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# Define platform specific output write paths
mountPrefix = expConfig.get(input_arguments.processingEnvironment, 'MOUNTPREFIX')
BASEOUTPUTDIR = expConfig.get(input_arguments.processingEnvironment, 'BASEOUTPUTDIR')
ExperimentBaseDirectoryPrefix = os.path.realpath(os.path.join(BASEOUTPUTDIR, ExperimentName))
ExperimentBaseDirectoryCache = ExperimentBaseDirectoryPrefix + "_CACHE"
ExperimentBaseDirectoryResults = ExperimentBaseDirectoryPrefix + "_Results"
if not os.path.exists(ExperimentBaseDirectoryCache):
os.makedirs(ExperimentBaseDirectoryCache)
if not os.path.exists(ExperimentBaseDirectoryResults):
os.makedirs(ExperimentBaseDirectoryResults)
if not PreviousExperimentName is None:
PreviousBaseDirectoryPrefix = os.path.realpath(os.path.join(BASEOUTPUTDIR, PreviousExperimentName))
PreviousBaseDirectoryResults = PreviousBaseDirectoryPrefix + "_Results"
assert os.path.exists(PreviousBaseDirectoryResults), "The previous experiment directory does not exist: {0}".format(PreviousBaseDirectoryResults)
else:
PreviousBaseDirectoryResults = None
# Define workup common reference data sets
# The ATLAS needs to be copied to the ExperimentBaseDirectoryPrefix
# The ATLAS pathing must stay constant
ATLASPATH = expConfig.get(input_arguments.processingEnvironment, 'ATLASPATH')
if not os.path.exists(ATLASPATH):
print("ERROR: Invalid Path for Atlas: {0}".format(ATLASPATH))
sys.exit(-1)
CACHE_ATLASPATH = os.path.realpath(os.path.join(ExperimentBaseDirectoryCache, 'Atlas'))
from distutils.dir_util import copy_tree
if not os.path.exists(CACHE_ATLASPATH):
print("Copying a reference of the atlas to the experiment cache directory:\n from: {0}\n to: {1}".format(ATLASPATH, CACHE_ATLASPATH))
copy_tree(ATLASPATH, CACHE_ATLASPATH, preserve_mode=1, preserve_times=1)
## Now generate the xml file with the correct pathing
file_replace(os.path.join(ATLASPATH, 'ExtendedAtlasDefinition.xml.in'), os.path.join(CACHE_ATLASPATH, 'ExtendedAtlasDefinition.xml'), "@ATLAS_DIRECTORY@", CACHE_ATLASPATH)
else:
print("Atlas already exists in experiment cache directory: {0}".format(CACHE_ATLASPATH))
CUSTOM_ENVIRONMENT = expConfig.get(input_arguments.processingEnvironment, 'CUSTOM_ENVIRONMENT')
CUSTOM_ENVIRONMENT = eval(CUSTOM_ENVIRONMENT)
## Set custom environmental variables so that subproceses work properly (i.e. for FreeSurfer)
# print CUSTOM_ENVIRONMENT
for key, value in CUSTOM_ENVIRONMENT.items():
# print "SETTING: ", key, value
os.putenv(key, value)
os.environ[key] = value
# print os.environ
# sys.exit(-1)
WORKFLOW_COMPONENTS_STRING = expConfig.get('EXPERIMENT_DATA', 'WORKFLOW_COMPONENTS')
WORKFLOW_COMPONENTS = eval(WORKFLOW_COMPONENTS_STRING)
## If freesurfer is requested, then ensure that a sane environment is available
if 'FREESURFER' in WORKFLOW_COMPONENTS:
print "FREESURFER NEEDS TO CHECK FOR SANE ENVIRONMENT HERE."
## Setup environment for CPU load balancing of ITK based programs.
total_CPUS = multiprocessing.cpu_count()
if input_arguments.wfrun == 'helium_all.q' or \
input_arguments.wfrun == 'helium_all.q_graph' or \
input_arguments.wfrun == 'ipl_OSX':
assert expConfig.getboolean(input_arguments.processingEnvironment, 'CLUSTER'), "CLUSTER section not set to true!"
CLUSTER_QUEUE, CLUSTER_QUEUE_LONG, QSTAT_IMMEDIATE_EXE, QSTAT_CACHED_EXE, MODULES = get_cluster_settings(expConfig)
elif input_arguments.wfrun == 'local_4':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 4)
elif input_arguments.wfrun == 'local_12':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 12)
elif input_arguments.wfrun == 'local':
# HACK
CLUSTER_QUEUE, CLUSTER_QUEUE_LONG, QSTAT_IMMEDIATE_EXE, QSTAT_CACHED_EXE, MODULES = get_cluster_settings(expConfig)
# END HACK
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 1)
elif input_arguments.wfrun == 'ds_runner':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 1)
else:
print "FAILED RUN: You must specify the run environment type. [helium_all.q,helium_all.q_graph,ipl_OSX,local_4,local_12,local,ds_runner]"
print input_arguments.wfrun
sys.exit(-1)
print "Configuring Pipeline"
## Ensure that entire db is built and cached before parallel section starts.
_ignoreme = OpenSubjectDatabase(ExperimentBaseDirectoryCache, [ "all" ], mountPrefix, subject_data_file)
to_do_subjects = input_arguments.subject.split(',')
if to_do_subjects[0] == "all":
to_do_subjects=_ignoreme.getAllSubjects()
_ignoreme = None
## Create the shell wrapper script for ensuring that all jobs running on remote hosts from SGE
# have the same environment as the job submission host.
JOB_SCRIPT = get_global_sge_script(sys.path, os.environ['PATH'], CUSTOM_ENVIRONMENT, MODULES)
print JOB_SCRIPT
# Randomly shuffle to_do_subjects to get max
import random
random.shuffle(to_do_subjects)
## Make a list of all the arguments to be processed
sp_args_list = list()
start_time=time.time()
subj_index = 1
for subjectid in to_do_subjects:
delay = 2.5*subj_index
subj_index += 1
print("START DELAY: {0}".format(delay))
sp_args=(CACHE_ATLASPATH, CLUSTER_QUEUE, CLUSTER_QUEUE_LONG,QSTAT_IMMEDIATE_EXE,QSTAT_CACHED_EXE,
ExperimentBaseDirectoryCache, ExperimentBaseDirectoryResults, subject_data_file,
GLOBAL_DATA_SINK_REWRITE, JOB_SCRIPT, WORKFLOW_COMPONENTS, input_arguments,
mountPrefix, start_time+delay, subjectid, PreviousBaseDirectoryResults)
sp_args_list.append(sp_args)
if 'local' in input_arguments.wfrun:
print("RUNNING WITHOUT POOL BUILDING")
for sp_args in sp_args_list:
DoSingleSubjectProcessing(sp_args)
else:
## Make a pool of workers to submit simultaneously
from multiprocessing import Pool
myPool = Pool(processes=64,maxtasksperchild=1)
all_results=myPool.map_async(DoSingleSubjectProcessing,sp_args_list).get(1e100)
for indx in range(0,len(sp_args_list)):
if all_results[indx] == False:
print "FAILED for {0}".format(sp_args_list[indx][-1])
print("THIS RUN OF BAW FOR SUBJS {0} HAS COMPLETED".format(to_do_subjects))
return 0
def setup_environment(argv):
"""
This function...
:param argv:
:return: environment, experiment, pipeline, cluster
"""
print("Configuring environment...")
import os
import os.path
from BAW.utilities.configFileParser import resolveDataSinkOption, parseFile
from BAW.utilities.pathHandling import validatePath
from BAW.utilities import misc
from collections import OrderedDict # Need OrderedDict internally to ensure consistent ordering
environment, experiment, pipeline, cluster = parseFile(
argv["--ExperimentConfig"], argv["--pe"], argv["--workphase"])
pipeline['ds_overwrite'] = resolveDataSinkOption(argv, pipeline)
if cluster is None:
print("Running on local")
# raise NotImplementedError("Running local has old code and has not been tested!")
# assert argv["--wfrun"] in argvWFRUN, \
# "wfrun options for clusters can only be given when the configuration file's CLUSTER option == True"
# os.environ['NSLOTS'] = str(misc.get_cpus(argv["--wf_template_runner"]))
else:
load_modules(cluster['modules']
) # Load modules if not already done ## MODS PATH
# print os.environ['LOADEDMODULES']
# if environment['virtualenv_dir'] is not None: # MODS PATH
# activate_this = validatePath(
# os.path.join(environment['virtualenv_dir'], 'bin', 'activate_this.py'), False, False)
# if os.path.exists( activate_this ) :
# exec(open(activate_this).read(), OrderedDict(__file__=activate_this))
utilities_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'utilities')
configure_env = validatePath(
os.path.join(utilities_path, 'configure_env.py'), False, False)
# Add the AutoWorkup directory to the PYTHONPATH every time - REQUIRED FOR CLUSTER DISPATCHING
environment['env']['PYTHONPATH'] = environment['env'][
'PYTHONPATH'] + ":" + os.path.dirname(__file__)
exec(
open(configure_env).read(),
OrderedDict(
__file__=__file__,
append_os_path=environment['env']['PATH'],
append_sys_path=environment['env']['PYTHONPATH'])) # MODS PATH
print(("@" * 80))
print((environment['env']['PYTHONPATH']))
print(("@" * 80))
print((environment['env']['PATH']))
print(("@" * 80))
from nipype import config
config.enable_debug_mode()
# config.enable_provenance()
from BAW.utilities.package_check import verify_packages
verify_packages()
if 'FREESURFER' in experiment['components']: # FREESURFER MODS
configure_FS = validatePath(
os.path.join(utilities_path, 'utilities', 'configure_FS.py'),
False, False)
exec(
open(configure_FS).read(),
OrderedDict(FS_VARS=misc.FS_VARS, env=environment['env']))
print("FREESURFER needs to check for sane environment here!"
) # TODO: raise warning, write method, what???
for key, value in list(environment['env'].items()):
if key in ['PATH', 'PYTHONPATH'] + misc.FS_VARS:
pass
else:
os.environ[
key] = value # Do not use os.putenv (see Python documentation)
return environment, experiment, pipeline, cluster
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 PreprocessingMALF( WFName,
inputDictionaries,
inputAtlasFilename,
outputDir
):
from nipype import config
config.enable_debug_mode()
print """ Run WarpSubjectsIntoAtlasWF"""
import os
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
from nipype.interfaces.utility import Function, IdentityInterface
from PipeLineFunctionHelpers import POSTERIORS
from WorkupT1T2TissueClassify import MakePosteriorDictionaryFunc
PreprocessingWF= pe.Workflow( name=WFName)
PreprocessingWF.base_dir = outputDir
#inputAtlasFilename = inputAtlasDir+ "template_t1_clipped.nii.gz"
"""
Clip Brain
"""
"""
- extract image list from dictionaries
"""
InputImageListND =pe.Node( interface = Function( function = ExtractImageList,
input_names = ['inputDictionaries',
'desiredKeys'],
output_names = ['outputImageList',
'outputTypeList']),
name='InputImageList')
InputImageListND.inputs.inputDictionaries = inputDictionaries
InputImageListND.inputs.desiredKeys = ['baselineImage', 'additionalImages', 'labelMap']
BrainClipND = pe.MapNode( interface = Function( function = ClipVolumeWithMask,
input_names = ['inputVolumeFilename',
'inputMaskFilename',
'inputType',
'outputVolumeFilename'] ,
output_names = ['outputType',
'outputVolume']),
iterfield=['inputVolumeFilename', 'inputType'],
name="BrainClip")
BrainClipND.inputs.inputMaskFilename = inputDictionaries[ 'brainMask' ]
PreprocessingWF.connect( [ (InputImageListND, BrainClipND, [ ('outputImageList', 'inputVolumeFilename') ] ),
(InputImageListND, BrainClipND, [ ('outputTypeList', 'inputType') ] )
] )
BrainClipND.inputs.outputVolumeFilename = "SubjectBrainClipped.nii.gz"
"""
Reconstruct input Dictionaries
"""
ReconstructDictND = pe.Node( interface = Function( function = ReconstructDictionary,
input_names = [ 'inputKeys','inputElements'],
output_names = [ 'outputDictionary'] ),
name = 'ReconstructDict' )
PreprocessingWF.connect( BrainClipND, 'outputType',
ReconstructDictND, 'inputKeys')
PreprocessingWF.connect( BrainClipND, 'outputVolume',
ReconstructDictND, 'inputElements')
"""
Warp all the subject image into atlas space
"""
WarpSubjectToAtlasND = pe.Node( interface = Function( function = WarpSubjectToAtlas,
input_names = [ 'WFName',
'inputDictionaries',
'inputAtlasFilename',
'outputDirectory' ],
output_names = [] ),
name = 'WarpSubjectToAtlas' )
WarpSubjectToAtlasND.inputs.WFName = 'WarpSubjectToAtlasWF'
PreprocessingWF.connect( ReconstructDictND, 'outputDictionary',
WarpSubjectToAtlasND, 'inputDictionaries')
WarpSubjectToAtlasND.inputs.inputAtlasFilename = inputAtlasFilename
WarpSubjectToAtlasND.inputs.outputDirectory = '.'
PreprocessingWF.write_graph()
PreprocessingWF.run()
def main(argv=None):
import argparse
import ConfigParser
import csv
import string
if argv == None:
argv = sys.argv
# Create and parse input arguments
parser = argparse.ArgumentParser(description='Runs a mini version of BuildTemplateParallel')
group = parser.add_argument_group('Required')
group.add_argument('-pe', action="store", dest='processingEnvironment', required=True,
help='The name of the processing environment to use from the config file')
group.add_argument('-wfrun', action="store", dest='wfrun', required=True,
help='The name of the workflow running plugin to use')
group.add_argument('-ExperimentConfig', action="store", dest='ExperimentConfig', required=True,
help='The path to the file that describes the entire experiment')
input_arguments = parser.parse_args()
expConfig = ConfigParser.ConfigParser()
expConfig.read(input_arguments.ExperimentConfig)
# Experiment specific information
session_db=expConfig.get('EXPERIMENT_DATA','SESSION_DB')
ExperimentName=expConfig.get('EXPERIMENT_DATA','EXPERIMENTNAME')
# Platform specific information
# Prepend the python search paths
PYTHON_AUX_PATHS=expConfig.get(input_arguments.processingEnvironment,'PYTHON_AUX_PATHS')
PYTHON_AUX_PATHS=PYTHON_AUX_PATHS.split(':')
PYTHON_AUX_PATHS.extend(sys.path)
sys.path=PYTHON_AUX_PATHS
# Prepend the shell environment search paths
PROGRAM_PATHS=expConfig.get(input_arguments.processingEnvironment,'PROGRAM_PATHS')
PROGRAM_PATHS=PROGRAM_PATHS.split(':')
PROGRAM_PATHS.extend(os.environ['PATH'].split(':'))
os.environ['PATH']=':'.join(PROGRAM_PATHS)
# Define platform specific output write paths
BASEOUTPUTDIR=expConfig.get(input_arguments.processingEnvironment,'BASEOUTPUTDIR')
ExperimentBaseDirectoryPrefix=os.path.realpath(os.path.join(BASEOUTPUTDIR,ExperimentName))
ExperimentBaseDirectoryCache=ExperimentBaseDirectoryPrefix+"_CACHE"
ExperimentBaseDirectoryResults=ExperimentBaseDirectoryPrefix +"_Results"
if not os.path.exists(ExperimentBaseDirectoryCache):
os.makedirs(ExperimentBaseDirectoryCache)
if not os.path.exists(ExperimentBaseDirectoryResults):
os.makedirs(ExperimentBaseDirectoryResults)
print os.environ
#sys.exit(-1)
CLUSTER_QUEUE=expConfig.get(input_arguments.processingEnvironment,'CLUSTER_QUEUE')
## Setup environment for CPU load balancing of ITK based programs.
import multiprocessing
total_CPUS=multiprocessing.cpu_count()
if input_arguments.wfrun == 'helium_all.q':
pass
elif input_arguments.wfrun == 'ipl_OSX':
pass
elif input_arguments.wfrun == 'local_4':
os.environ['NSLOTS']="{0}".format(total_CPUS/4)
elif input_arguments.wfrun == 'local_3':
os.environ['NSLOTS']="{0}".format(total_CPUS/3)
elif input_arguments.wfrun == 'local_12':
os.environ['NSLOTS']="{0}".format(total_CPUS/12)
elif input_arguments.wfrun == 'local':
os.environ['NSLOTS']="{0}".format(total_CPUS/1)
else:
print "You must specify the run environment type. [helium_all.q,ipl_OSX,local_3,local_4,local_12,local]"
print input_arguments.wfrun
sys.exit(-1)
print "Configuring Pipeline"
from nipype import config ## NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() ## NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
import buildTemplateParallelDriver ## NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
btp=buildTemplateParallelDriver.BuildTemplateParallelWorkFlow(
ExperimentBaseDirectoryCache,
ExperimentBaseDirectoryResults,
session_db)
print "Start Processing"
## Create the shell wrapper script for ensuring that all jobs running on remote hosts from SGE
# have the same environment as the job submission host.
JOB_SCRIPT=get_global_sge_script(sys.path,PROGRAM_PATHS)
print JOB_SCRIPT
SGEFlavor='SGE'
if input_arguments.wfrun == 'helium_all.q':
btp.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT,qsub_args="-S /bin/bash -pe smp1 1-4 -l mem_free=4000M -o /dev/null -e /dev/null "+CLUSTER_QUEUE))
if input_arguments.wfrun == 'helium_all.q_graph':
SGEFlavor='SGEGraph' #Use the SGEGraph processing
btp.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT,qsub_args="-S /bin/bash -pe smp1 1-4 -l mem_free=4000M -o /dev/null -e /dev/null "+CLUSTER_QUEUE))
elif input_arguments.wfrun == 'ipl_OSX':
btp.write_graph()
print "Running On ipl_OSX"
btp.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT,qsub_args="-S /bin/bash -pe smp1 1-4 -l mem_free=4000M -o /dev/null -e /dev/null "+CLUSTER_QUEUE))
elif input_arguments.wfrun == 'local_4':
btp.write_graph()
print "Running with 4 parallel processes on local machine"
btp.run(plugin='MultiProc', plugin_args={'n_procs' : 4})
elif input_arguments.wfrun == 'local_3':
btp.write_graph()
print "Running with 3 parallel processes on local machine"
btp.run(plugin='MultiProc', plugin_args={'n_procs' : 3})
elif input_arguments.wfrun == 'local_12':
btp.write_graph()
print "Running with 12 parallel processes on local machine"
btp.run(plugin='MultiProc', plugin_args={'n_procs' : 12})
elif input_arguments.wfrun == 'local':
try:
btp.write_graph()
except:
pass
print "Running sequentially on local machine"
btp.run()
else:
print "You must specify the run environment type. [helium_all.q,ipl_OSX,local_3,local_4,local_12,local]"
print input_arguments.wfrun
sys.exit(-1)
def unitWorkUp ( configurationFilename,
doApply = False,
baseDir = "."):
import os
import sys
import nipype.pipeline.engine as pe
from nipype.interfaces.utility import Function
import ConfigurationParser
import crossValidationUnit as this
from nipype import config
config.enable_debug_mode()
workflow = pe.Workflow( name = 'balancedTraning' )
workflow.base_dir = baseDir
configurationMap = ConfigurationParser.ConfigurationSectionMap( configurationFilename)
Options = configurationMap[ 'Options' ]
roiDict = Options[ 'roiBooleanCreator'.lower() ]
#
#-------------------------------- filenameGeneratorND is dummy node
# to create proper probability file location for nipype
#
filenameGeneratorND = pe.Node( name = "filenameGeneratorND",
interface = Function(
input_names = ['roiList',
'gaussianSigma'],
output_names = ['probabilityMapFilename'],
function = this.getProbabilityMapFilename )
)
filenameGeneratorND.inputs.roiList = roiDict.keys()
#
#-------------------------------- start from generate probability
#
probabilityMapGeneratorND = pe.Node( name = "probabilityMapGeneratorND",
interface = Function(
input_names = ['configurationFilename',
'probabilityMapDict',
'gaussianSigma',
'outputXmlFilename'],
output_names = [ 'probabilityMapDict',
'outputXmlFilename',
'outputConfigurationFilename'],
function = ConfigurationParser.BRAINSCutGenerateProbabilityMap )
)
probabilityMapGeneratorND.inputs.outputXmlFilename = 'netConfiguration.xml'
probabilityMapGeneratorND.inputs.configurationFilename = configurationFilename
probabilityMapGeneratorND.inputs.gaussianSigma = Options[ 'gaussianSigma'.lower() ]
workflow.connect( filenameGeneratorND, 'probabilityMapFilename',
probabilityMapGeneratorND, 'probabilityMapDict' )
#
#-------------------------------- create vectors for each ROI
#
configFileND = pe.Node( name = "configFileND",
interface = Function(
input_names = ['originalFilename',
'editedFilenamePrefix' ],
output_names = ['editiedFilenames'],
function = ConfigurationParser.ConfigurationFileEditor )
)
configFileND.inputs.originalFilename = configurationFilename
configFileND.inputs.editedFilenamePrefix = 'ROI'
workflow.add_nodes( [ configFileND ] )
vectorCreatorND = pe.MapNode( name = "vectorCreatorND",
interface = Function(
input_names = ['configurationFilename',
'probabilityMapDict',
'normalization',
'outputXmlFilename',
'outputVectorFilename'],
output_names = ['outputVectorFilename',
'outputVectorHdrFilename',
'outputNormalization',
'outputXmlFilename'],
function = ConfigurationParser.BRAINSCutCreateVector ),
iterfield = [ 'configurationFilename']
)
vectorCreatorND.inputs.outputVectorFilename = 'oneROIVectorFile.txt'
vectorCreatorND.inputs.outputXmlFilename = 'oneROICreateVectorNetConfiguration.xml'
import ast
normalizationOption = Options[ 'normalization'.lower()]
#normalizationOption = ast.literal_eval( Options[ 'normalization'.lower()] )
print( """Normalization Option: {str}
""".format( str=normalizationOption ) )
vectorCreatorND.iterables = ( 'normalization', normalizationOption )
#
#-------------------------------- workflow connections
#
workflow.connect( configFileND, 'editiedFilenames',
vectorCreatorND, 'configurationFilename' )
workflow.connect( probabilityMapGeneratorND, 'probabilityMapDict',
vectorCreatorND, 'probabilityMapDict' )
#
#-------------------------------- balance and combine each ROI vectors
#
balaceND = pe.Node( name = "balanceND",
interface = Function(
input_names = ['inputVectorFilenames'],
output_names = ['outputVectorFilenames',
'outputVectorHdrFilenames'],
function = ConfigurationParser.BalanceInputVectors )
)
workflow.connect( vectorCreatorND, 'outputVectorFilename',
balaceND, 'inputVectorFilenames' )
combineND = pe.Node( name = "combineND",
interface = Function(
input_names = ['inputVectorFilenames',
'outputVectorFilename'],
output_names = ['outputVectorFilename',
'outputVectorHdrFilename'],
function = ConfigurationParser.CombineInputVectors )
)
workflow.connect( balaceND, 'outputVectorFilenames',
combineND, 'inputVectorFilenames')
combineND.inputs.outputVectorFilename = 'allCombinedVector.txtANN'
#
#-------------------------------- train
#
trainND = pe.Node( name = "trainND",
interface = Function(
input_names = ['configurationFilename',
'inputVectorFilename',
'outputModelFilenamePrefix',
'outputXmlFilename',
'methodParameter'],
output_names = ['outputTrainedModelFilename',
'outputMethodParameter'],
function = ConfigurationParser.BRAINSCutTrainModel ),
)
#methodParameter = { '--method': 'RandomForest',
# '--numberOfTrees': 60,
# '--randomTreeDepth ': 60 }
import ast
methodFromConfiguFile = Options['modelParameter'.lower()]
trainND.iterables= ( 'methodParameter', methodFromConfiguFile )
trainND.inputs.outputXmlFilename = 'trianNetConfiguration.xml'
trainND.inputs.outputModelFilenamePrefix = 'trainModelFile.txt'
workflow.connect( probabilityMapGeneratorND, 'outputConfigurationFilename',
trainND, 'configurationFilename')
workflow.connect( combineND, 'outputVectorFilename',
trainND, 'inputVectorFilename')
#
#-------------------------------- apply
#
# make output dir for each subject as a
if doApply:
applyND = pe.Node( name = "applyND",
interface = Function(
input_names = ['configurationFilename',
'probabilityMapDict',
'normalization',
'inputModelFilename',
'methodParameter',
'outputXmlFilename'
],
output_names = ['outputLabelDict'],
function = ConfigurationParser.BRAINSCutApplyModel )
)
#methodParameter = { '--method': 'RandomForest',
# '--numberOfTrees': 60,
# '--randomTreeDepth ': 60 }
applyND.inputs.outputXmlFilename = 'applyConfiguration.xml'
workflow.connect( probabilityMapGeneratorND, 'outputConfigurationFilename',
applyND, 'configurationFilename')
workflow.connect( vectorCreatorND, 'outputNormalization',
applyND, 'normalization' )
workflow.connect( probabilityMapGeneratorND, 'probabilityMapDict',
applyND, 'probabilityMapDict' )
workflow.connect( trainND, 'outputTrainedModelFilename',
applyND, 'inputModelFilename' )
workflow.connect( trainND, 'outputMethodParameter',
applyND, 'methodParameter' )
#
# analysis
#
#analysisND = pe.Node( name = "analysisND",
# interface = Function(
# input_names['inputImageDict',
# 'inputManualDict',
# 'outputCSVFilename'],
# output_names['outputCSVFilename'],
# function = analysis.similarityFromApplyOutput )
# )
#
#
##workflow.run(updatehash=True)
workflow.run()
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 run_workflow():
raise Exception("This code was not tested after refactoring to be used by "
"preprocessing_workflow.py.")
config.enable_debug_mode()
# ------------------ Specify variables
ds_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
data_dir = ds_root
output_dir = 'func_unwarp'
working_dir = 'workingdirs/func_unwarp'
subject_list = ['eddy']
session_list = ['20170511']
# ------------------ Input Files
infosource = Node(IdentityInterface(fields=[
'subject_id',
'session_id',
]),
name="infosource")
infosource.iterables = [
('session_id', session_list),
('subject_id', subject_list),
]
# SelectFiles
templates = {
'funcs':
'resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/func/'
'sub-{subject_id}_ses-{session_id}_'
'task-*_bold_res-1x1x1_preproc.nii.gz',
# Use *-roi for testing
# 'task-curvetracing_run-01_bold_res-1x1x1_preproc-roi.nii.gz',
'fmap_phasediff':
'resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/fmap/'
'sub-{subject_id}_ses-{session_id}_phasediff_res-1x1x1_preproc'
'.nii.gz',
'fmap_magnitude':
'resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/fmap/'
'sub-{subject_id}_ses-{session_id}_magnitude1_res-1x1x1_preproc'
'.nii.gz',
'fmap_mask':
'transformed-manual-fmap-mask/sub-{subject_id}/ses-{session_id}/fmap/'
'sub-{subject_id}_ses-{session_id}_'
'magnitude1_res-1x1x1_preproc.nii.gz',
}
inputfiles = Node(nio.SelectFiles(templates, base_directory=data_dir),
name="input_files")
# ------------------ Output Files
# Datasink
outputfiles = Node(nio.DataSink(base_directory=ds_root,
container=output_dir,
parameterization=True),
name="output_files")
# Use the following DataSink output substitutions
outputfiles.inputs.substitutions = [
('subject_id_', 'sub-'),
('session_id_', 'ses-'),
('/undistorted/', '/'),
('/undistorted_masks/', '/'),
('_unwarped.nii.gz', '.nii.gz'),
('phasediff_radians_unwrapped_mask', '_rec-unwrapped_phasediff'),
]
outputfiles.inputs.regexp_substitutions = [
(r'_fugue[0-9]+/', r'func/'), (r'_undistort_masks[0-9]+/', r'func/'),
(r'_ses-([a-zA-Z0-9]*)_sub-([a-zA-Z0-9]*)', r'sub-\2/ses-\1')
]
# -------------------------------------------- Create Pipeline
workflow = Workflow(name='undistort',
base_dir=os.path.join(ds_root, working_dir))
workflow.connect([(infosource, inputfiles, [('subject_id', 'subject_id'),
('session_id', 'session_id')])
])
undistort_flow = create_workflow()
# Connect sub-workflow inputs
workflow.connect([(inputfiles, undistort_flow, [
('subject_id', 'in.subject_id'),
('session_id', 'in.session_id'),
('fmap_phasediff', 'in.fmap_phasediff'),
('fmap_magnitude', 'in.fmap_magnitude'),
('fmap_mask', 'in.fmap_mask'),
]), (undistort_flow, outputfiles, [
('out.unwarped_file', 'undistorted'),
])])
workflow.connect(undistort_flow, 'unwarped_file', outputfiles,
'undistorted')
workflow.connect(undistort_masks, 'unwarped_file', outputfiles,
'undistorted_masks')
workflow.stop_on_first_crash = True
workflow.keep_inputs = True
workflow.remove_unnecessary_outputs = False
workflow.write_graph()
workflow.run()
def setup_environment(argv):
print "Configuring environment..."
import os
import os.path
from utilities.configFileParser import resolveDataSinkOption, parseFile
from utilities.pathHandling import validatePath
from utilities import misc
environment, experiment, pipeline, cluster = parseFile(
argv["--ExperimentConfig"], argv["--pe"], argv["--workphase"]
)
pipeline["ds_overwrite"] = resolveDataSinkOption(argv, pipeline)
if cluster is None:
print "Running on local"
# raise NotImplementedError("Running local has old code and has not been tested!")
# assert argv["--wfrun"] in argvWFRUN, \
# "wfrun options for clusters can only be given when the configuration file's CLUSTER option == True"
# os.environ['NSLOTS'] = str(misc.get_cpus(argv["--wf_template_runner"]))
else:
load_modules(cluster["modules"]) # Load modules if not already done ## MODS PATH
# print os.environ['LOADEDMODULES']
if environment["virtualenv_dir"] is not None: # MODS PATH
activate_this = validatePath(
os.path.join(environment["virtualenv_dir"], "bin", "activate_this.py"), False, False
)
execfile(activate_this, dict(__file__=activate_this))
utilities_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "utilities")
configure_env = validatePath(os.path.join(utilities_path, "configure_env.py"), False, False)
# Add the AutoWorkup directory to the PYTHONPATH every time - REQUIRED FOR CLUSTER DISPATCHING
environment["env"]["PYTHONPATH"] = environment["env"]["PYTHONPATH"] + ":" + os.path.dirname(__file__)
execfile(
configure_env,
dict(
__file__=__file__,
append_os_path=environment["env"]["PATH"],
append_sys_path=environment["env"]["PYTHONPATH"],
),
) # MODS PATH
print ("@" * 80)
print environment["env"]["PYTHONPATH"]
print ("@" * 80)
print environment["env"]["PATH"]
print ("@" * 80)
from nipype import config
config.enable_debug_mode()
from utilities.package_check import verify_packages
verify_packages()
if "FREESURFER" in experiment["components"]: # FREESURFER MODS
configure_FS = validatePath(os.path.join(utilities_path, "utilities", "configure_FS.py"), False, False)
execfile(configure_FS, dict(FS_VARS=misc.FS_VARS, env=environment["env"]))
print "FREESURFER needs to check for sane environment here!" # TODO: raise warning, write method, what???
for key, value in environment["env"].items():
if key in ["PATH", "PYTHONPATH"] + misc.FS_VARS:
pass
else:
os.environ[key] = value # Do not use os.putenv (see Python documentation)
return environment, experiment, pipeline, cluster
from __future__ import print_function
__author__ = 'johnsonhj'
######################################################################################
###### Now ensure that all the required packages can be read in from this custom path
# \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# print sys.path
from nipype import config # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
# config.enable_provenance()
##############################################################################
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory
from nipype.interfaces.base import traits, isdefined, BaseInterface
from nipype.interfaces.utility import Merge, Split, Function, Rename, IdentityInterface
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
import nipype.interfaces.io as nio # Data i/o
from nipype.interfaces.freesurfer import ReconAll
from nipype.utils.misc import package_check
# package_check('nipype', '5.4', 'tutorial1') ## HACK: Check nipype version
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
import os
from __future__ import print_function
__author__ = 'johnsonhj'
######################################################################################
###### Now ensure that all the required packages can be read in from this custom path
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# print sys.path
from nipype import config # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
#config.enable_provenance()
##############################################################################
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory
from nipype.interfaces.base import traits, isdefined, BaseInterface
from nipype.interfaces.utility import Merge, Split, Function, Rename, IdentityInterface
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
import nipype.interfaces.io as nio # Data i/o
from nipype.interfaces.freesurfer import ReconAll
from nipype.utils.misc import package_check
# package_check('nipype', '5.4', 'tutorial1') ## HACK: Check nipype version
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
import os
## Check to ensure that SimpleITK can be found
#import SimpleITK as sitk
def run_workflow(csv_file, use_pbs, contrasts_name, template):
workflow = pe.Workflow(name='run_level1flow')
workflow.base_dir = os.path.abspath('./workingdirs')
from nipype import config, logging
config.update_config({
'logging': {
'log_directory': os.path.join(workflow.base_dir, 'logs'),
'log_to_file': True,
'workflow_level': 'DEBUG',
'interface_level': 'DEBUG',
}
})
logging.update_logging(config)
config.enable_debug_mode()
# redundant with enable_debug_mode() ...
workflow.stop_on_first_crash = True
workflow.remove_unnecessary_outputs = False
workflow.keep_inputs = True
workflow.hash_method = 'content'
"""
Setup the contrast structure that needs to be evaluated. This is a list of
lists. The inner list specifies the contrasts and has the following format:
[Name,Stat,[list of condition names],[weights on those conditions]. The
condition names must match the `names` listed in the `evt_info` function
described above.
"""
try:
import importlib
mod = importlib.import_module('contrasts.' + contrasts_name)
contrasts = mod.contrasts
# event_names = mod.event_names
except ImportError:
raise RuntimeError('Unknown contrasts: %s. Must exist as a Python'
' module in contrasts directory!' % contrasts_name)
modelfit = create_workflow(contrasts)
import bids_templates as bt
inputnode = pe.Node(niu.IdentityInterface(fields=[
'subject_id',
'session_id',
'run_id',
]),
name='input')
assert csv_file is not None, "--csv argument must be defined!"
reader = niu.CSVReader()
reader.inputs.header = True
reader.inputs.in_file = csv_file
out = reader.run()
subject_list = out.outputs.subject
session_list = out.outputs.session
run_list = out.outputs.run
inputnode.iterables = [
('subject_id', subject_list),
('session_id', session_list),
('run_id', run_list),
]
inputnode.synchronize = True
templates = {
'funcs':
'derivatives/featpreproc/highpassed_files/sub-{subject_id}/ses-{session_id}/func/'
'sub-{subject_id}_ses-{session_id}_*_run-{run_id}*_bold_res-1x1x1_preproc_*.nii.gz',
# 'funcmasks':
# 'featpreproc/func_unwarp/sub-{subject_id}/ses-{session_id}/func/'
# 'sub-{subject_id}_ses-{session_id}_*_run-{run_id}*_bold_res-1x1x1_preproc'
# '_mc_unwarped.nii.gz',
'highpass':
'******'
'sub-{subject_id}_ses-{session_id}_*_run-{run_id}_bold_res-1x1x1_preproc_*.nii.gz',
'motion_parameters':
'derivatives/featpreproc/motion_corrected/sub-{subject_id}/ses-{session_id}/func/'
'sub-{subject_id}_ses-{session_id}_*_run-{run_id}_bold_res-1x1x1_preproc.param.1D',
'motion_outlier_files':
'derivatives/featpreproc/motion_outliers/sub-{subject_id}/ses-{session_id}/func/'
'art.sub-{subject_id}_ses-{session_id}_*_run-{run_id}_bold_res-1x1x1_preproc_mc'
'_maths_outliers.txt',
'event_log':
'sub-{subject_id}/ses-{session_id}/func/'
# 'sub-{subject_id}_ses-{session_id}*_bold_res-1x1x1_preproc'
'sub-{subject_id}_ses-{session_id}*run-{run_id}*'
# '.nii.gz',
'_events.tsv',
'ref_func':
'derivatives/featpreproc/reference/func/*.nii.gz',
'ref_funcmask':
'derivatives/featpreproc/reference/func_mask/*.nii.gz',
}
inputfiles = pe.Node(nio.SelectFiles(templates, base_directory=data_dir),
name='in_files')
workflow.connect([
(inputnode, inputfiles, [
('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id'),
]),
])
join_input = pe.JoinNode(
niu.IdentityInterface(fields=[
# 'subject_id',
# 'session_id',
# 'run_id',
'funcs',
'highpass',
'motion_parameters',
'motion_outlier_files',
'event_log',
'ref_func',
'ref_funcmask',
]),
joinsource='input',
joinfield=[
'funcs',
'highpass',
'motion_parameters',
'motion_outlier_files',
'event_log',
],
# unique=True,
name='join_input')
workflow.connect([
(inputfiles, join_input, [
('funcs', 'funcs'),
('highpass', 'highpass'),
('motion_parameters', 'motion_parameters'),
('motion_outlier_files', 'motion_outlier_files'),
('event_log', 'event_log'),
('ref_func', 'ref_func'),
('ref_funcmask', 'ref_funcmask'),
]),
(join_input, modelfit, [
('funcs', 'inputspec.funcs'),
('highpass', 'inputspec.highpass'),
('motion_parameters', 'inputspec.motion_parameters'),
('motion_outlier_files', 'inputspec.motion_outlier_files'),
('event_log', 'inputspec.event_log'),
('ref_func', 'inputspec.ref_func'),
('ref_funcmask', 'inputspec.ref_funcmask'),
]),
])
modelfit.inputs.inputspec.fwhm = 2.0
modelfit.inputs.inputspec.highpass = 50
modelfit.write_graph(simple_form=True)
modelfit.write_graph(graph2use='orig', format='png', simple_form=True)
# modelfit.write_graph(graph2use='detailed', format='png', simple_form=False)
workflow.stop_on_first_crash = True
workflow.keep_inputs = True
workflow.remove_unnecessary_outputs = False
workflow.write_graph(simple_form=True)
workflow.write_graph(graph2use='colored', format='png', simple_form=True)
# workflow.write_graph(graph2use='detailed', format='png', simple_form=False)
if use_pbs:
workflow.run(plugin='PBS',
plugin_args={'template': os.path.expanduser(template)})
else:
workflow.run()
#!/usr/bin/env python3
# http://nipype.readthedocs.io/en/latest/users/examples/fmri_fsl.html
# http://miykael.github.io/nipype-beginner-s-guide/firstSteps.html#input-output-stream
import os # system functions
import nipype.interfaces.io as nio # Data i/o
import nipype.interfaces.fsl as fsl # fsl
import nipype.interfaces.afni as afni # afni
from nipype.interfaces.utility import IdentityInterface
from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype import config
config.enable_debug_mode()
from subcode.afni_allin_slices import AFNIAllinSlices
# the create_workflow_allin_slices workflow is the only one that's used
def create_workflow_allin_slices(name='motion_correction', iterfield=['in_file']):
workflow = Workflow(name=name)
inputs = Node(IdentityInterface(fields=[
'subject_id',
'session_id',
'ref_func',
'ref_func_weights',
'funcs',
'funcs_masks',
def main(argv=None):
import argparse
import ConfigParser
import csv
import string
if argv == None:
argv = sys.argv
# Create and parse input arguments
parser = argparse.ArgumentParser(description='Runs a mini version of BRAINSAutoWorkup')
group = parser.add_argument_group('Required')
group.add_argument('-pe', action="store", dest='processingEnvironment', required=True,
help='The name of the processing environment to use from the config file')
group.add_argument('-wfrun', action="store", dest='wfrun', required=True,
help='The name of the workflow running plugin to use')
group.add_argument('-subject', action="store", dest='subject', required=True,
help='The name of the subject to process')
group.add_argument('-ExperimentConfig', action="store", dest='ExperimentConfig', required=True,
help='The path to the file that describes the entire experiment')
parser.add_argument('-doshort', action='store', dest='doshort', default=False, help='If not present, do long')
parser.add_argument('-rewrite_datasinks', action='store_true', default=False,
help='Use if the datasinks should be forced rerun.\nDefault: value in configuration file')
parser.add_argument('--version', action='version', version='%(prog)s 1.0')
input_arguments = parser.parse_args()
expConfig = ConfigParser.ConfigParser()
expConfig.read(input_arguments.ExperimentConfig)
# Pipeline-specific information
GLOBAL_DATA_SINK_REWRITE_FROM_CONFIG = expConfig.getboolean('PIPELINE', 'GLOBAL_DATA_SINK_REWRITE')
GLOBAL_DATA_SINK_REWRITE=setDataSinkRewriteValue(input_arguments.rewrite_datasinks, GLOBAL_DATA_SINK_REWRITE_FROM_CONFIG)
# Experiment specific information
subject_data_file = expConfig.get('EXPERIMENT_DATA', 'SESSION_DB')
ExperimentName = expConfig.get('EXPERIMENT_DATA', 'EXPERIMENTNAME')
WORKFLOW_COMPONENTS_STRING = expConfig.get('EXPERIMENT_DATA', 'WORKFLOW_COMPONENTS')
WORKFLOW_COMPONENTS = eval(WORKFLOW_COMPONENTS_STRING)
# Platform specific information
# Prepend the python search paths
PYTHON_AUX_PATHS = expConfig.get(input_arguments.processingEnvironment, 'PYTHON_AUX_PATHS')
PYTHON_AUX_PATHS = PYTHON_AUX_PATHS.split(':')
PYTHON_AUX_PATHS.extend(sys.path)
sys.path = PYTHON_AUX_PATHS
######################################################################################
###### Now ensure that all the required packages can be read in from this custom path
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
# print sys.path
from nipype import config # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
config.enable_debug_mode() # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
##############################################################################
from nipype.interfaces.base import CommandLine, CommandLineInputSpec, TraitedSpec, File, Directory
from nipype.interfaces.base import traits, isdefined, BaseInterface
from nipype.interfaces.utility import Merge, Split, Function, Rename, IdentityInterface
import nipype.interfaces.io as nio # Data i/o
import nipype.pipeline.engine as pe # pypeline engine
from nipype.interfaces.freesurfer import ReconAll
from nipype.utils.misc import package_check
# package_check('nipype', '5.4', 'tutorial1') ## HACK: Check nipype version
package_check('numpy', '1.3', 'tutorial1')
package_check('scipy', '0.7', 'tutorial1')
package_check('networkx', '1.0', 'tutorial1')
package_check('IPython', '0.10', 'tutorial1')
## Check to ensure that SimpleITK can be found
import SimpleITK as sitk
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
#####################################################################################
# FreeSurfer is extraordinarly finicky and is easily confused and incorrect.
# Force that all the FREESURFER env vars are set in subsequent scripts by
# ensuring that rough versions of these environmental variables are not
# set internal to this script.
prohibited_env_var_exists = False
for ENVVAR_TO_CHECK in ['FREESURFER_HOME','FSFAST_HOME','FSF_OUTPUT_FORMAT','SUBJECTS_DIR','MNI_DIR','FSL_DIR']:
if os.environ.has_key(ENVVAR_TO_CHECK):
prohibited_env_var_exists = True
print( "ERROR: Environmental Variable {0}={1} exists. Please unset before continuing.".format(ENVVAR_TO_CHECK,os.environ[ENVVAR_TO_CHECK]) )
if prohibited_env_var_exists:
sys.exit(-1)
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
#####################################################################################
# Prepend the shell environment search paths
PROGRAM_PATHS = expConfig.get(input_arguments.processingEnvironment, 'PROGRAM_PATHS')
PROGRAM_PATHS = PROGRAM_PATHS.split(':')
PROGRAM_PATHS.extend(os.environ['PATH'].split(':'))
os.environ['PATH'] = ':'.join(PROGRAM_PATHS)
# Define platform specific output write paths
mountPrefix = expConfig.get(input_arguments.processingEnvironment, 'MOUNTPREFIX')
BASEOUTPUTDIR = expConfig.get(input_arguments.processingEnvironment, 'BASEOUTPUTDIR')
ExperimentBaseDirectoryPrefix = os.path.realpath(os.path.join(BASEOUTPUTDIR, ExperimentName))
ExperimentBaseDirectoryCache = ExperimentBaseDirectoryPrefix + "_CACHE"
ExperimentBaseDirectoryResults = ExperimentBaseDirectoryPrefix + "_Results"
if not os.path.exists(ExperimentBaseDirectoryCache):
os.makedirs(ExperimentBaseDirectoryCache)
if not os.path.exists(ExperimentBaseDirectoryResults):
os.makedirs(ExperimentBaseDirectoryResults)
# Define workup common reference data sets
# The ATLAS needs to be copied to the ExperimentBaseDirectoryPrefix
# The ATLAS pathing must stay constant
ATLASPATH = expConfig.get(input_arguments.processingEnvironment, 'ATLASPATH')
if not os.path.exists(ATLASPATH):
print("ERROR: Invalid Path for Atlas: {0}".format(ATLASPATH))
sys.exit(-1)
CACHE_ATLASPATH = os.path.realpath(os.path.join(ExperimentBaseDirectoryCache, 'Atlas'))
from distutils.dir_util import copy_tree
if not os.path.exists(CACHE_ATLASPATH):
print("Copying a reference of the atlas to the experiment cache directory:\n from: {0}\n to: {1}".format(ATLASPATH, CACHE_ATLASPATH))
copy_tree(ATLASPATH, CACHE_ATLASPATH, preserve_mode=1, preserve_times=1)
## Now generate the xml file with the correct pathing
file_replace(os.path.join(ATLASPATH, 'ExtendedAtlasDefinition.xml.in'), os.path.join(CACHE_ATLASPATH, 'ExtendedAtlasDefinition.xml'), "@ATLAS_DIRECTORY@", CACHE_ATLASPATH)
else:
print("Atlas already exists in experiment cache directory: {0}".format(CACHE_ATLASPATH))
# Just to be safe, copy the model file as well
BCDMODELPATH = expConfig.get(input_arguments.processingEnvironment, 'BCDMODELPATH')
CACHE_BCDMODELPATH = os.path.join(ExperimentBaseDirectoryCache, os.path.basename(BCDMODELPATH))
from distutils.file_util import copy_file
for BCDModelFile in ['LLSModel-2ndVersion.h5', 'T1-2ndVersion.mdl']:
if BCDModelFile[-2:] == 'h5':
BCDModelFile = os.path.join('Transforms_h5', BCDModelFile)
orig = os.path.join(BCDMODELPATH, BCDModelFile)
new = os.path.join(CACHE_BCDMODELPATH, BCDModelFile)
new = new.replace('Transforms_h5/', '') # Flatten back out, even if you needed to get files from subdirectory.
if not os.path.exists(CACHE_BCDMODELPATH):
os.mkdir(CACHE_BCDMODELPATH)
if not os.path.exists(new):
print("Copying BCD Model file to cache directory: {0}".format(new))
copy_file(orig, new, preserve_mode=1, preserve_times=1)
else:
print("BCD Model exists in cache directory: {0}".format(new))
CUSTOM_ENVIRONMENT = expConfig.get(input_arguments.processingEnvironment, 'CUSTOM_ENVIRONMENT')
CUSTOM_ENVIRONMENT = eval(CUSTOM_ENVIRONMENT)
## Set custom environmental variables so that subproceses work properly (i.e. for FreeSurfer)
# print CUSTOM_ENVIRONMENT
for key, value in CUSTOM_ENVIRONMENT.items():
# print "SETTING: ", key, value
os.putenv(key, value)
os.environ[key] = value
# print os.environ
# sys.exit(-1)
## If freesurfer is requested, then ensure that a sane environment is available
if 'FREESURFER' in WORKFLOW_COMPONENTS:
print "FREESURFER NEEDS TO CHECK FOR SANE ENVIRONMENT HERE."
CLUSTER_QUEUE = expConfig.get(input_arguments.processingEnvironment, 'CLUSTER_QUEUE')
CLUSTER_QUEUE_LONG = expConfig.get(input_arguments.processingEnvironment, 'CLUSTER_QUEUE_LONG')
## Setup environment for CPU load balancing of ITK based programs.
import multiprocessing
total_CPUS = multiprocessing.cpu_count()
if input_arguments.wfrun == 'helium_all.q':
pass
elif input_arguments.wfrun == 'helium_all.q_graph':
pass
elif input_arguments.wfrun == 'ipl_OSX':
pass
elif input_arguments.wfrun == 'local_4':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 4)
elif input_arguments.wfrun == 'local_12':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 12)
elif input_arguments.wfrun == 'local':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 1)
elif input_arguments.wfrun == 'ds_runner':
os.environ['NSLOTS'] = "{0}".format(total_CPUS / 1)
else:
print "FAILED RUN: You must specify the run environment type. [helium_all.q,helium_all.q_graph,ipl_OSX,local_4,local_12,local,ds_runner]"
print input_arguments.wfrun
sys.exit(-1)
print "Configuring Pipeline"
import SessionDB
subjectDatabaseFile = os.path.join(ExperimentBaseDirectoryCache, 'InternalWorkflowSubjectDB.db')
subject_list = input_arguments.subject.split(',')
## TODO: Only make DB if db is older than subject_data_file.
if (not os.path.exists(subjectDatabaseFile)) or (os.path.getmtime(subjectDatabaseFile) < os.path.getmtime(subject_data_file)):
ExperimentDatabase = SessionDB.SessionDB(subjectDatabaseFile, subject_list)
ExperimentDatabase.MakeNewDB(subject_data_file, mountPrefix)
ExperimentDatabase = None
ExperimentDatabase = SessionDB.SessionDB(subjectDatabaseFile, subject_list)
else:
print("Using cached database, {0}".format(subjectDatabaseFile))
ExperimentDatabase = SessionDB.SessionDB(subjectDatabaseFile, subject_list)
print "ENTIRE DB for {_subjid}: ".format(_subjid=ExperimentDatabase.getSubjectFilter())
print "^^^^^^^^^^^^^"
for row in ExperimentDatabase.getEverything():
print row
print "^^^^^^^^^^^^^"
## Create the shell wrapper script for ensuring that all jobs running on remote hosts from SGE
# have the same environment as the job submission host.
JOB_SCRIPT = get_global_sge_script(sys.path, PROGRAM_PATHS, CUSTOM_ENVIRONMENT)
print JOB_SCRIPT
import WorkupT1T2 # NOTE: This needs to occur AFTER the PYTHON_AUX_PATHS has been modified
print "TESTER"
import ShortWorkupT1T2
for subjectid in ExperimentDatabase.getAllSubjects():
if input_arguments.doshort:
baw200 = ShortWorkupT1T2.ShortWorkupT1T2(subjectid, mountPrefix,
os.path.join(ExperimentBaseDirectoryCache, str(subjectid)),
ExperimentBaseDirectoryResults,
ExperimentDatabase,
CACHE_ATLASPATH,
CACHE_BCDMODELPATH,
GLOBAL_DATA_SINK_REWRITE,
WORKFLOW_COMPONENTS=WORKFLOW_COMPONENTS, CLUSTER_QUEUE=CLUSTER_QUEUE, CLUSTER_QUEUE_LONG=CLUSTER_QUEUE_LONG)
else:
baw200 = WorkupT1T2.WorkupT1T2(subjectid, mountPrefix,
os.path.join(ExperimentBaseDirectoryCache, str(subjectid)),
ExperimentBaseDirectoryResults,
ExperimentDatabase,
CACHE_ATLASPATH,
CACHE_BCDMODELPATH,
GLOBAL_DATA_SINK_REWRITE,
WORKFLOW_COMPONENTS=WORKFLOW_COMPONENTS, CLUSTER_QUEUE=CLUSTER_QUEUE, CLUSTER_QUEUE_LONG=CLUSTER_QUEUE_LONG, SGE_JOB_SCRIPT=JOB_SCRIPT)
print "Start Processing"
SGEFlavor = 'SGE'
try:
if input_arguments.wfrun == 'helium_all.q':
try:
baw200.write_graph()
except:
pass
baw200.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT, qsub_args="-S /bin/bash -cwd -pe smp1 1-12 -l h_vmem=19G,mem_free=9G -o /dev/null -e /dev/null " + CLUSTER_QUEUE))
elif input_arguments.wfrun == 'helium_all.q_graph':
try:
baw200.write_graph()
except:
pass
SGEFlavor = 'SGEGraph' # Use the SGEGraph processing
baw200.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT, qsub_args="-S /bin/bash -cwd -pe smp1 1-12 -l h_vmem=19G,mem_free=9G -o /dev/null -e /dev/null " + CLUSTER_QUEUE))
elif input_arguments.wfrun == 'ipl_OSX':
try:
baw200.write_graph()
except:
pass
print "Running On ipl_OSX"
baw200.run(plugin=SGEFlavor,
plugin_args=dict(template=JOB_SCRIPT, qsub_args="-S /bin/bash -cwd -pe smp1 1-12 -l h_vmem=19G,mem_free=9G -o /dev/null -e /dev/null " + CLUSTER_QUEUE))
elif input_arguments.wfrun == 'local_4':
try:
baw200.write_graph()
except:
pass
print "Running with 4 parallel processes on local machine"
baw200.run(plugin='MultiProc', plugin_args={'n_procs': 4})
elif input_arguments.wfrun == 'local_12':
try:
baw200.write_graph()
except:
pass
print "Running with 12 parallel processes on local machine"
baw200.run(plugin='MultiProc', plugin_args={'n_procs': 12})
elif input_arguments.wfrun == 'ds_runner':
class ds_runner(object):
def run(self, graph, **kwargs):
for node in graph.nodes():
if '_ds' in node.name.lower():
node.run()
baw200.run(plugin=ds_runner())
elif input_arguments.wfrun == 'local':
try:
baw200.write_graph()
except:
pass
print "Running sequentially on local machine"
# baw200.run(updatehash=True)
baw200.run()
else:
print "You must specify the run environment type. [helium_all.q,helium_all.q_graph,ipl_OSX,local_4,local_12,local]"
print input_arguments.wfrun
sys.exit(-1)
except Exception, err:
print("ERROR: EXCEPTION CAUGHT IN RUNNING SUBJECT {0}".format(subjectid))
raise err
# Literal translation of the Perl script https://github.com/andrewjanke/volgenmodel
# to Python and using Nipype interfaces where possible.
#
# Currently this code only runs on a single core as it does not take advantage
# of Nipype's workflow functionality. Future versions will support this.
# Author: Carlo Hamalainen <*****@*****.**>
from nipype import config
config.enable_debug_mode()
import os
import os.path
import subprocess
# import nipype.pipeline.engine as pe
# import nipype.interfaces.io as nio
# import nipype.interfaces.utility as utils
from nipypeminc import \
Volcentre, \
Norm, \
Volpad, \
Voliso, \
Math, \
Pik, \
Blur, \
Gennlxfm, \
XfmConcat, \
BestLinReg, \
NlpFit, \
XfmAvg, \
def build_collect_workflow(args, retval):
import os
import glob
import warnings
warnings.filterwarnings("ignore")
import ast
import pkg_resources
from pathlib import Path
import yaml
import uuid
from time import strftime
import shutil
try:
import pynets
print(f"\n\nPyNets Version:\n{pynets.__version__}\n\n")
except ImportError:
print("PyNets not installed! Ensure that you are using the correct"
" python version.")
# Set Arguments to global variables
resources = args.pm
if resources == "auto":
from multiprocessing import cpu_count
import psutil
nthreads = cpu_count() - 1
procmem = [
int(nthreads),
int(list(psutil.virtual_memory())[4] / 1000000000)
]
else:
procmem = list(eval(str(resources)))
plugin_type = args.plug
if isinstance(plugin_type, list):
plugin_type = plugin_type[0]
verbose = args.v
working_path = args.basedir
work_dir = args.work
modality = args.modality
drop_cols = args.dc
if isinstance(modality, list):
modality = modality[0]
if os.path.isdir(work_dir):
shutil.rmtree(work_dir)
os.makedirs(f"{str(Path(working_path))}/{modality}_group_topology_auc",
exist_ok=True)
wf = collect_all(working_path, modality, drop_cols)
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
"r") as stream:
try:
hardcoded_params = yaml.load(stream)
runtime_dict = {}
execution_dict = {}
for i in range(len(hardcoded_params["resource_dict"])):
runtime_dict[list(hardcoded_params["resource_dict"][i].keys(
))[0]] = ast.literal_eval(
list(hardcoded_params["resource_dict"][i].values())[0][0])
for i in range(len(hardcoded_params["execution_dict"])):
execution_dict[list(
hardcoded_params["execution_dict"][i].keys())[0]] = list(
hardcoded_params["execution_dict"][i].values())[0][0]
except FileNotFoundError:
print("Failed to parse runconfig.yaml")
run_uuid = f"{strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4()}"
os.makedirs(f"{work_dir}/pynets_out_collection{run_uuid}", exist_ok=True)
wf.base_dir = f"{work_dir}/pynets_out_collection{run_uuid}"
if verbose is True:
from nipype import config, logging
cfg_v = dict(
logging={
"workflow_level": "DEBUG",
"utils_level": "DEBUG",
"interface_level": "DEBUG",
"filemanip_level": "DEBUG",
"log_directory": str(wf.base_dir),
"log_to_file": True,
},
monitoring={
"enabled": True,
"sample_frequency": "0.1",
"summary_append": True,
"summary_file": str(wf.base_dir),
},
)
logging.update_logging(config)
config.update_config(cfg_v)
config.enable_debug_mode()
config.enable_resource_monitor()
import logging
callback_log_path = f"{wf.base_dir}{'/run_stats.log'}"
logger = logging.getLogger("callback")
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(callback_log_path)
logger.addHandler(handler)
execution_dict["crashdump_dir"] = str(wf.base_dir)
execution_dict["plugin"] = str(plugin_type)
cfg = dict(execution=execution_dict)
for key in cfg.keys():
for setting, value in cfg[key].items():
wf.config[key][setting] = value
try:
wf.write_graph(graph2use="colored", format="png")
except BaseException:
pass
if verbose is True:
from nipype.utils.profiler import log_nodes_cb
plugin_args = {
"n_procs": int(procmem[0]),
"memory_gb": int(procmem[1]),
"status_callback": log_nodes_cb,
"scheduler": "mem_thread",
}
else:
plugin_args = {
"n_procs": int(procmem[0]),
"memory_gb": int(procmem[1]),
"scheduler": "mem_thread",
}
print("%s%s%s" % ("\nRunning with ", str(plugin_args), "\n"))
wf.run(plugin=plugin_type, plugin_args=plugin_args)
if verbose is True:
from nipype.utils.draw_gantt_chart import generate_gantt_chart
print("Plotting resource profile from run...")
generate_gantt_chart(callback_log_path, cores=int(procmem[0]))
handler.close()
logger.removeHandler(handler)
return
