/
cpac_pipeline.py
1349 lines (1136 loc) · 50.9 KB
/
cpac_pipeline.py
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# Copyright (C) 2022 C-PAC Developers
#
# This file is part of CPAC-Development and is a modified version of a
# file that is part of C-PAC.
#
# CPAC-Development and C-PAC are free software: you can redistribute
# them and/or modify them under the terms of the GNU Lesser General
# Public License as published by the Free Software Foundation, either
# version 3 of the License, or (at your option) any later version.
#
# CPAC-Development and C-PAC are distributed in the hope that they will
# be useful, but WITHOUT ANY WARRANTY; without even the implied
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See
# the GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with CPAC-Development or C-PAC. If not, see
# <https://www.gnu.org/licenses/>.
import os
import time
import csv
import shutil
import pickle
import copy
import yaml
import logging as cb_logging
from time import strftime
import nipype
from CPAC.pipeline import nipype_pipeline_engine as pe
from CPAC.pipeline.plugins import LegacyMultiProcPlugin
from nipype import config
from nipype import logging
from indi_aws import aws_utils, fetch_creds
import CPAC
from CPAC.pipeline.engine import NodeBlock, initiate_rpool, wrap_block
from CPAC.anat_preproc.anat_preproc import (
freesurfer_preproc,
freesurfer_abcd_preproc,
anatomical_init,
acpc_align_head,
acpc_align_head_with_mask,
acpc_align_brain,
acpc_align_brain_with_mask,
registration_T2w_to_T1w,
non_local_means,
n4_bias_correction,
t1t2_bias_correction,
brain_mask_afni,
brain_mask_fsl,
brain_mask_niworkflows_ants,
brain_mask_unet,
brain_mask_freesurfer,
brain_mask_freesurfer_abcd,
brain_mask_freesurfer_fsl_tight,
brain_mask_freesurfer_fsl_loose,
brain_mask_acpc_afni,
brain_mask_acpc_fsl,
brain_mask_acpc_niworkflows_ants,
brain_mask_acpc_unet,
brain_mask_acpc_freesurfer,
brain_mask_acpc_freesurfer_abcd,
brain_extraction,
correct_restore_brain_intensity_abcd,
brain_mask_acpc_freesurfer_fsl_tight,
brain_mask_acpc_freesurfer_fsl_loose,
brain_extraction,
brain_extraction_temp,
brain_extraction,
anatomical_init_T2,
acpc_align_head_T2,
acpc_align_head_with_mask_T2,
acpc_align_brain_T2,
acpc_align_brain_with_mask_T2,
non_local_means_T2,
n4_bias_correction_T2,
brain_mask_T2,
brain_mask_acpc_T2,
brain_extraction_temp_T2,
brain_extraction_T2
)
from CPAC.registration.registration import (
register_ANTs_anat_to_template,
overwrite_transform_anat_to_template,
register_FSL_anat_to_template,
register_symmetric_ANTs_anat_to_template,
register_symmetric_FSL_anat_to_template,
register_ANTs_EPI_to_template,
register_FSL_EPI_to_template,
coregistration_prep_vol,
coregistration_prep_mean,
coregistration_prep_fmriprep,
coregistration,
create_func_to_T1template_xfm,
create_func_to_T1template_symmetric_xfm,
warp_timeseries_to_T1template,
warp_bold_mean_to_T1template,
warp_bold_mask_to_T1template,
warp_deriv_mask_to_T1template,
warp_timeseries_to_EPItemplate,
warp_bold_mean_to_EPItemplate,
warp_bold_mask_to_EPItemplate,
warp_deriv_mask_to_EPItemplate,
warp_timeseries_to_T1template_abcd,
single_step_resample_timeseries_to_T1template,
warp_timeseries_to_T1template_dcan_nhp
)
from CPAC.seg_preproc.seg_preproc import (
tissue_seg_fsl_fast,
tissue_seg_T1_template_based,
tissue_seg_EPI_template_based,
tissue_seg_ants_prior,
tissue_seg_freesurfer
)
from CPAC.func_preproc.func_preproc import (
func_scaling,
func_truncate,
func_despike,
func_slice_time,
func_reorient,
bold_mask_afni,
bold_mask_fsl,
bold_mask_fsl_afni,
bold_mask_anatomical_refined,
bold_mask_anatomical_based,
bold_mask_anatomical_resampled,
bold_mask_ccs,
bold_masking,
func_mean,
func_normalize,
func_mask_normalize,
get_motion_ref,
func_motion_estimates,
motion_estimate_filter,
calc_motion_stats,
func_motion_correct_only,
func_motion_correct
)
from CPAC.distortion_correction.distortion_correction import (
distcor_phasediff_fsl_fugue,
distcor_blip_afni_qwarp
)
from CPAC.nuisance.nuisance import (
ICA_AROMA_ANTsreg,
ICA_AROMA_FSLreg,
ICA_AROMA_ANTsEPIreg,
ICA_AROMA_FSLEPIreg,
nuisance_regressors_generation,
nuisance_regression,
erode_mask_T1w,
erode_mask_CSF,
erode_mask_GM,
erode_mask_WM,
nuisance_regressors_generation_EPItemplate,
erode_mask_bold,
erode_mask_boldCSF,
erode_mask_boldGM,
erode_mask_boldWM
)
from CPAC.surface.surf_preproc import surface_preproc
from CPAC.timeseries.timeseries_analysis import (
timeseries_extraction_AVG,
timeseries_extraction_Voxel,
spatial_regression
)
from CPAC.sca.sca import (
SCA_AVG,
dual_regression,
multiple_regression
)
from CPAC.alff.alff import alff_falff
from CPAC.reho.reho import reho
from CPAC.vmhc.vmhc import (
smooth_func_vmhc,
warp_timeseries_to_sym_template,
vmhc
)
from CPAC.network_centrality.pipeline import (
network_centrality
)
from CPAC.utils.datasource import (
gather_extraction_maps
)
from CPAC.pipeline.schema import valid_options
from CPAC.utils.trimmer import the_trimmer
from CPAC.utils import Configuration
from CPAC.qc.pipeline import create_qc_workflow
from CPAC.qc.utils import generate_qc_pages
from CPAC.utils.utils import (
check_config_resources,
check_system_deps,
)
from CPAC.utils.monitoring import log_nodes_initial
from CPAC.utils.monitoring.draw_gantt_chart import resource_report
logger = logging.getLogger('nipype.workflow')
# config.enable_debug_mode()
def run_workflow(sub_dict, c, run, pipeline_timing_info=None, p_name=None,
plugin='MultiProc', plugin_args=None, test_config=False):
'''
Function to prepare and, optionally, run the C-PAC workflow
Parameters
----------
sub_dict : dictionary
subject dictionary with anatomical and functional image paths
c : Configuration object
CPAC pipeline configuration dictionary object
run : boolean
flag to indicate whether to run the prepared workflow
pipeline_timing_info : list (optional); default=None
list of pipeline info for reporting timing information
p_name : string (optional); default=None
name of pipeline
plugin : string (optional); defaule='MultiProc'
nipype plugin to utilize when the workflow is ran
plugin_args : dictionary (optional); default=None
plugin-specific arguments for the workflow plugin
Returns
-------
workflow : nipype workflow
the prepared nipype workflow object containing the parameters
specified in the config
'''
# Assure that changes on config will not affect other parts
c = copy.copy(c)
subject_id = sub_dict['subject_id']
if sub_dict['unique_id']:
subject_id += "_" + sub_dict['unique_id']
c['subject_id'] = subject_id
log_dir = os.path.join(c.pipeline_setup['log_directory']['path'],
f'pipeline_{c.pipeline_setup["pipeline_name"]}',
subject_id)
if not os.path.exists(log_dir):
os.makedirs(os.path.join(log_dir))
shutil.copyfile('/CRITICAL_ALERT.rst',
os.path.join(log_dir, 'CRITICAL_ALERT.rst'))
# TODO ASH Enforce c.run_logging to be boolean
# TODO ASH Schema validation
config.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': bool(getattr(c.pipeline_setup['log_directory'],
'run_logging', True))
},
'execution': {
'crashfile_format': 'txt',
'resource_monitor_frequency': 0.2
}
})
config.enable_resource_monitor()
logging.update_logging(config)
# Start timing here
pipeline_start_time = time.time()
# at end of workflow, take timestamp again, take time elapsed and check
# tempfile add time to time data structure inside tempfile, and increment
# number of subjects
# Check pipeline config resources
sub_mem_gb, num_cores_per_sub, num_ants_cores, num_omp_cores = check_config_resources(
c)
if plugin_args:
plugin_args['memory_gb'] = sub_mem_gb
plugin_args['n_procs'] = num_cores_per_sub
else:
plugin_args = {'memory_gb': sub_mem_gb, 'n_procs': num_cores_per_sub}
# perhaps in future allow user to set threads maximum
# this is for centrality mostly
# import mkl
os.environ['OMP_NUM_THREADS'] = str(num_omp_cores)
os.environ['MKL_NUM_THREADS'] = '1' # str(num_cores_per_sub)
os.environ['ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS'] = str(num_ants_cores)
# TODO: TEMPORARY
# TODO: solve the UNet model hanging issue during MultiProc
if "UNet" in c.anatomical_preproc['brain_extraction']['using']:
c.pipeline_setup['system_config']['max_cores_per_participant'] = 1
logger.info("\n\n[!] LOCKING CPUs PER PARTICIPANT TO 1 FOR U-NET "
"MODEL.\n\nThis is a temporary measure due to a known "
"issue preventing Nipype's parallelization from running "
"U-Net properly.\n\n")
# calculate maximum potential use of cores according to current pipeline
# configuration
max_core_usage = int(
c.pipeline_setup['system_config']['max_cores_per_participant']) * \
int(c.pipeline_setup['system_config'][
'num_participants_at_once'])
try:
creds_path = sub_dict['creds_path']
if creds_path and 'none' not in creds_path.lower():
if os.path.exists(creds_path):
input_creds_path = os.path.abspath(creds_path)
else:
err_msg = 'Credentials path: "%s" for subject "%s" was not ' \
'found. Check this path and try again.' % (
creds_path, subject_id)
raise Exception(err_msg)
else:
input_creds_path = None
except KeyError:
input_creds_path = None
# TODO enforce value with schema validation
try:
encrypt_data = bool(
config.pipeline_setup['Amazon-AWS']['s3_encryption'])
except:
encrypt_data = False
information = """
C-PAC version: {cpac_version}
Setting maximum number of cores per participant to {cores}
Setting number of participants at once to {participants}
Setting OMP_NUM_THREADS to {omp_threads}
Setting MKL_NUM_THREADS to 1
Setting ANTS/ITK thread usage to {ants_threads}
Maximum potential number of cores that might be used during this run: {max_cores}
"""
execution_info = """
End of subject workflow {workflow}
CPAC run complete:
Pipeline configuration: {pipeline}
Subject workflow: {workflow}
Elapsed run time (minutes): {elapsed}
Timing information saved in {log_dir}/cpac_individual_timing_{pipeline}.csv
System time of start: {run_start}
System time of completion: {run_finish}
{critical_alert}
"""
with open('/CRITICAL_ALERT.rst', 'r') as critical_alert_file:
critical_alert = '\n'.join([
f' {line.rstrip()}' for line in
critical_alert_file.readlines() if not line.startswith('.. ')])
logger.info(information.format(
cpac_version=CPAC.__version__,
cores=c.pipeline_setup['system_config']['max_cores_per_participant'],
participants=c.pipeline_setup['system_config'][
'num_participants_at_once'],
omp_threads=c.pipeline_setup['system_config']['num_OMP_threads'],
ants_threads=c.pipeline_setup['system_config']['num_ants_threads'],
max_cores=max_core_usage
))
subject_info = {}
subject_info['subject_id'] = subject_id
subject_info['start_time'] = pipeline_start_time
check_centrality_degree = c.network_centrality['run'] and \
(len(c.network_centrality['degree_centrality'][
'weight_options']) != 0 or \
len(c.network_centrality[
'eigenvector_centrality'][
'weight_options']) != 0)
check_centrality_lfcd = c.network_centrality['run'] and \
len(c.network_centrality[
'local_functional_connectivity_density'][
'weight_options']) != 0
# Check system dependencies
check_ica_aroma = c.nuisance_corrections['1-ICA-AROMA']['run']
if isinstance(check_ica_aroma, list):
check_ica_aroma = True in check_ica_aroma
check_system_deps(check_ants='ANTS' in c.registration_workflows[
'anatomical_registration']['registration']['using'],
check_ica_aroma=check_ica_aroma,
check_centrality_degree=check_centrality_degree,
check_centrality_lfcd=check_centrality_lfcd)
# absolute paths of the dirs
c.pipeline_setup['working_directory']['path'] = os.path.abspath(
c.pipeline_setup['working_directory']['path'])
if 's3://' not in c.pipeline_setup['output_directory']['path']:
c.pipeline_setup['output_directory']['path'] = os.path.abspath(
c.pipeline_setup['output_directory']['path'])
workflow = build_workflow(
subject_id, sub_dict, c, p_name, num_ants_cores
)
if test_config:
logger.info('This has been a test of the pipeline configuration '
'file, the pipeline was built successfully, but was '
'not run')
else:
working_dir = os.path.join(
c.pipeline_setup['working_directory']['path'], workflow.name)
# if c.write_debugging_outputs:
# with open(os.path.join(working_dir, 'resource_pool.pkl'), 'wb') as f:
# pickle.dump(strat_list, f)
# if c.pipeline_setup['working_directory']['regenerate_outputs'] is True:
# erasable = list(find_files(working_dir, '*sink*')) + \
# list(find_files(working_dir, '*link*')) + \
# list(find_files(working_dir, '*log*'))
# for f in erasable:
# if os.path.isfile(f):
# os.remove(f)
# else:
# shutil.rmtree(f)
if hasattr(c, 'trim') and c.trim:
logger.warn("""
Trimming is an experimental feature, and if used wrongly, it can lead to unreproducible results.
It is useful for performance optimization, but only if used correctly.
Please, make yourself aware of how it works and its assumptions:
- The pipeline configuration has not changed;
- The data configuration / BIDS directory has not changed;
- The files from the output directory has not changed;
- Your softwares versions has not changed;
- Your C-PAC version has not changed;
- You do not have access to the working directory.
""")
workflow, _ = the_trimmer(
workflow,
output_dir=c.pipeline_setup['output_directory']['path'],
s3_creds_path=input_creds_path,
)
pipeline_start_datetime = strftime("%Y-%m-%d %H:%M:%S")
try:
subject_info['resource_pool'] = []
# for strat_no, strat in enumerate(strat_list):
# strat_label = 'strat_%d' % strat_no
# subject_info[strat_label] = strat.get_name()
# subject_info['resource_pool'].append(strat.get_resource_pool())
subject_info['status'] = 'Running'
# Create callback logger
cb_log_filename = os.path.join(log_dir,
'callback.log')
try:
if not os.path.exists(os.path.dirname(cb_log_filename)):
os.makedirs(os.path.dirname(cb_log_filename))
except IOError:
pass
# Add handler to callback log file
cb_logger = cb_logging.getLogger('callback')
cb_logger.setLevel(cb_logging.DEBUG)
handler = cb_logging.FileHandler(cb_log_filename)
cb_logger.addHandler(handler)
# Log initial information from all the nodes
log_nodes_initial(workflow)
# Add status callback function that writes in callback log
if nipype.__version__ not in ('1.5.1'):
err_msg = "This version of Nipype may not be compatible with " \
"CPAC v%s, please install Nipype version 1.5.1\n" \
% (CPAC.__version__)
logger.error(err_msg)
if plugin_args['n_procs'] == 1:
plugin = 'Linear'
if not plugin:
plugin = LegacyMultiProcPlugin(plugin_args)
try:
# Actually run the pipeline now, for the current subject
workflow.run(plugin=plugin, plugin_args=plugin_args)
except UnicodeDecodeError:
raise EnvironmentError(
"C-PAC migrated from Python 2 to Python 3 in v1.6.2 (see "
"release notes). Your working directory contains Python 2 "
"pickles, probably from an older version of C-PAC. If you "
"want to continue to use this working directory, run\n\n"
"docker run -i --rm --user $(id -u):$(id -g) "
"-v /path/to/working_dir:/working "
"fcpindi/c-pac:latest /bids_dir /outputs cli -- "
"utils repickle /working\n"
"\nor\n\n"
"singularity run "
"C-PAC_latest.sif /bids_dir /outputs cli -- "
"utils repickle /path/to/working_dir\n\n"
"before running C-PAC >=v1.6.2"
)
# PyPEER kick-off
# if c.PyPEER['run']:
# from CPAC.pypeer.peer import prep_for_pypeer
# prep_for_pypeer(c.PyPEER['eye_scan_names'], c.PyPEER['data_scan_names'],
# c.PyPEER['eye_mask_path'], c.pipeline_setup['output_directory']['path'], subject_id,
# pipeline_ids, c.PyPEER['stimulus_path'], c.PyPEER['minimal_nuisance_correction']['peer_gsr'],
# c.PyPEER['minimal_nuisance_correction']['peer_scrub'], c.PyPEER['minimal_nuisance_correction']['scrub_thresh'])
# Dump subject info pickle file to subject log dir
subject_info['status'] = 'Completed'
subject_info_file = os.path.join(
log_dir, 'subject_info_%s.pkl' % subject_id
)
with open(subject_info_file, 'wb') as info:
pickle.dump(list(subject_info), info)
# have this check in case the user runs cpac_runner from terminal and
# the timing parameter list is not supplied as usual by the GUI
if pipeline_timing_info != None:
# pipeline_timing_info list:
# [0] - unique pipeline ID
# [1] - pipeline start time stamp (first click of 'run' from GUI)
# [2] - number of subjects in subject list
unique_pipeline_id = pipeline_timing_info[0]
pipeline_start_stamp = pipeline_timing_info[1]
num_subjects = pipeline_timing_info[2]
# elapsed time data list:
# [0] - elapsed time in minutes
elapsed_time_data = []
elapsed_time_data.append(
int(((time.time() - pipeline_start_time) / 60)))
# elapsedTimeBin list:
# [0] - cumulative elapsed time (minutes) across all subjects
# [1] - number of times the elapsed time has been appended
# (effectively a measure of how many subjects have run)
# TODO
# write more doc for all this
# warning in .csv that some runs may be partial
# code to delete .tmp file
timing_temp_file_path = os.path.join(
c.pipeline_setup['log_directory']['path'],
'%s_pipeline_timing.tmp' % unique_pipeline_id)
if not os.path.isfile(timing_temp_file_path):
elapsedTimeBin = []
elapsedTimeBin.append(0)
elapsedTimeBin.append(0)
with open(timing_temp_file_path, 'wb') as handle:
pickle.dump(elapsedTimeBin, handle)
with open(timing_temp_file_path, 'rb') as handle:
elapsedTimeBin = pickle.loads(handle.read())
elapsedTimeBin[0] = elapsedTimeBin[0] + elapsed_time_data[0]
elapsedTimeBin[1] = elapsedTimeBin[1] + 1
with open(timing_temp_file_path, 'wb') as handle:
pickle.dump(elapsedTimeBin, handle)
# this happens once the last subject has finished running!
if elapsedTimeBin[1] == num_subjects:
pipelineTimeDict = {}
pipelineTimeDict['Pipeline'] = c.pipeline_setup[
'pipeline_name']
pipelineTimeDict['Cores_Per_Subject'] = \
c.pipeline_setup['system_config'][
'max_cores_per_participant']
pipelineTimeDict['Simultaneous_Subjects'] = \
c.pipeline_setup['system_config'][
'num_participants_at_once']
pipelineTimeDict['Number_of_Subjects'] = num_subjects
pipelineTimeDict['Start_Time'] = pipeline_start_stamp
pipelineTimeDict['End_Time'] = strftime(
"%Y-%m-%d_%H:%M:%S")
pipelineTimeDict['Elapsed_Time_(minutes)'] = \
elapsedTimeBin[0]
pipelineTimeDict['Status'] = 'Complete'
gpaTimeFields = [
'Pipeline', 'Cores_Per_Subject',
'Simultaneous_Subjects',
'Number_of_Subjects', 'Start_Time',
'End_Time', 'Elapsed_Time_(minutes)',
'Status'
]
timeHeader = dict(zip(gpaTimeFields, gpaTimeFields))
with open(os.path.join(
c.pipeline_setup['log_directory']['path'],
'cpac_individual_timing_%s.csv' %
c.pipeline_setup['pipeline_name']
), 'a') as timeCSV, open(os.path.join(
c.pipeline_setup['log_directory']['path'],
'cpac_individual_timing_%s.csv' %
c.pipeline_setup['pipeline_name']
), 'r') as readTimeCSV:
timeWriter = csv.DictWriter(timeCSV,
fieldnames=gpaTimeFields)
timeReader = csv.DictReader(readTimeCSV)
headerExists = False
for line in timeReader:
if 'Start_Time' in line:
headerExists = True
if headerExists == False:
timeWriter.writerow(timeHeader)
timeWriter.writerow(pipelineTimeDict)
# remove the temp timing file now that it is no longer needed
os.remove(timing_temp_file_path)
# Upload logs to s3 if s3_str in output directory
if c.pipeline_setup['output_directory'][
'path'].lower().startswith('s3://'):
try:
# Store logs in s3 output director/logs/...
s3_log_dir = os.path.join(
c.pipeline_setup['output_directory']['path'],
'logs',
os.path.basename(log_dir)
)
bucket_name = \
c.pipeline_setup['output_directory']['path'].split('/')[2]
bucket = fetch_creds.return_bucket(creds_path,
bucket_name)
# Collect local log files
local_log_files = []
for root, _, files in os.walk(log_dir):
local_log_files.extend([os.path.join(root, fil)
for fil in files])
# Form destination keys
s3_log_files = [loc.replace(log_dir, s3_log_dir)
for loc in local_log_files]
# Upload logs
aws_utils.s3_upload(bucket,
(local_log_files, s3_log_files),
encrypt=encrypt_data)
# Delete local log files
for log_f in local_log_files:
os.remove(log_f)
except Exception as exc:
err_msg = 'Unable to upload CPAC log files in: %s.\nError: %s'
logger.error(err_msg, log_dir, exc)
except Exception as e:
import traceback;
traceback.print_exc()
execution_info = """
Error of subject workflow {workflow}
CPAC run error:
Pipeline configuration: {pipeline}
Subject workflow: {workflow}
Elapsed run time (minutes): {elapsed}
Timing information saved in {log_dir}/cpac_individual_timing_{pipeline}.csv
System time of start: {run_start}
{critical_alert}
"""
finally:
if workflow:
resource_report(cb_log_filename,
num_cores_per_sub, logger)
logger.info(execution_info.format(
workflow=workflow.name,
pipeline=c.pipeline_setup['pipeline_name'],
log_dir=c.pipeline_setup['log_directory']['path'],
elapsed=(time.time() - pipeline_start_time) / 60,
run_start=pipeline_start_datetime,
run_finish=strftime("%Y-%m-%d %H:%M:%S"),
critical_alert=critical_alert
))
# Remove working directory when done
if c.pipeline_setup['working_directory'][
'remove_working_dir']:
try:
if os.path.exists(working_dir):
logger.info("Removing working dir: %s",
working_dir)
shutil.rmtree(working_dir)
except (FileNotFoundError, PermissionError):
logger.warn('Could not remove working directory %s',
working_dir)
def initialize_nipype_wf(cfg, sub_data_dct, name=""):
if name:
name = f'_{name}'
workflow_name = f'cpac{name}_{sub_data_dct["subject_id"]}_{sub_data_dct["unique_id"]}'
wf = pe.Workflow(name=workflow_name)
wf.base_dir = cfg.pipeline_setup['working_directory']['path']
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(cfg.pipeline_setup['log_directory'][
'path'])
}
return wf
def load_cpac_pipe_config(pipe_config):
# Load in pipeline config file
config_file = os.path.realpath(pipe_config)
try:
if not os.path.exists(config_file):
raise IOError
else:
cfg = Configuration(yaml.safe_load(open(config_file, 'r')))
except IOError:
print("config file %s doesn't exist" % config_file)
raise
except yaml.parser.ParserError as e:
error_detail = "\"%s\" at line %d" % (
e.problem,
e.problem_mark.line
)
raise Exception(
"Error parsing config file: {0}\n\n"
"Error details:\n"
" {1}"
"\n\n".format(config_file, error_detail)
)
except Exception as e:
raise Exception(
"Error parsing config file: {0}\n\n"
"Error details:\n"
" {1}"
"\n\n".format(config_file, e)
)
return cfg
def build_anat_preproc_stack(rpool, cfg, pipeline_blocks=None):
if not pipeline_blocks:
pipeline_blocks = []
# T1w Anatomical Preprocessing
if not rpool.check_rpool('desc-reorient_T1w'):
anat_init_blocks = [
anatomical_init
]
pipeline_blocks += anat_init_blocks
pipeline_blocks += [freesurfer_preproc]
if not rpool.check_rpool('desc-preproc_T1w'):
# brain masking for ACPC alignment
if cfg.anatomical_preproc['acpc_alignment']['acpc_target'] == 'brain':
if rpool.check_rpool('space-T1w_desc-brain_mask') or \
cfg.surface_analysis['freesurfer']['run']:
acpc_blocks = [
brain_extraction_temp,
acpc_align_brain_with_mask
# outputs space-T1w_desc-brain_mask for later - keep the mask (the user provided)
]
acpc_blocks.append(
[brain_mask_acpc_freesurfer_fsl_tight,
brain_mask_acpc_freesurfer_fsl_loose]
)
else:
acpc_blocks = [
[brain_mask_acpc_afni,
brain_mask_acpc_fsl,
brain_mask_acpc_niworkflows_ants,
brain_mask_acpc_unet,
brain_mask_acpc_freesurfer_abcd],
# brain_mask_acpc_freesurfer
# we don't want these masks to be used later
brain_extraction_temp,
acpc_align_brain
]
elif cfg.anatomical_preproc['acpc_alignment'][
'acpc_target'] == 'whole-head':
if rpool.check_rpool('space-T1w_desc-brain_mask') or \
cfg.surface_analysis['freesurfer']['run']:
acpc_blocks = [
acpc_align_head_with_mask
# outputs space-T1w_desc-brain_mask for later - keep the mask (the user provided)
]
else:
acpc_blocks = [
acpc_align_head # does not output nor generate a mask
]
anat_preproc_blocks = [
(non_local_means, ('T1w', ['desc-preproc_T1w',
'desc-reorient_T1w',
'T1w'])),
n4_bias_correction
]
if cfg.anatomical_preproc['acpc_alignment']['run_before_preproc']:
anat_blocks = acpc_blocks + anat_preproc_blocks
else:
anat_blocks = anat_preproc_blocks + acpc_blocks
pipeline_blocks += anat_blocks
pipeline_blocks += [freesurfer_abcd_preproc]
# Anatomical T1 brain masking
if not rpool.check_rpool('space-T1w_desc-brain_mask') or \
cfg.surface_analysis['freesurfer']['run']:
anat_brain_mask_blocks = [
[brain_mask_afni,
brain_mask_fsl,
brain_mask_niworkflows_ants,
brain_mask_unet,
brain_mask_freesurfer_abcd,
brain_mask_freesurfer_fsl_tight,
brain_mask_freesurfer_fsl_loose]
# brain_mask_freesurfer
]
pipeline_blocks += anat_brain_mask_blocks
# T2w Anatomical Preprocessing
if rpool.check_rpool('T2w'):
if not rpool.check_rpool('desc-reorient_T2w'):
anat_init_blocks_T2 = [
anatomical_init_T2
]
pipeline_blocks += anat_init_blocks_T2
# TODO: T2 freesurfer_preproc?
# pipeline_blocks += [freesurfer_preproc]
if not rpool.check_rpool('desc-preproc_T2w'):
# brain masking for ACPC alignment
if cfg.anatomical_preproc['acpc_alignment']['acpc_target'] == 'brain':
if rpool.check_rpool('space-T2w_desc-brain_mask'):
acpc_blocks_T2 = [
brain_extraction_temp_T2,
acpc_align_brain_with_mask_T2
# outputs space-T2w_desc-brain_mask for later - keep the mask (the user provided)
]
else:
acpc_blocks_T2 = [
brain_mask_acpc_T2,
# we don't want these masks to be used later, only used in brain_extraction_temp_T2
brain_extraction_temp_T2,
acpc_align_brain_T2
]
elif cfg.anatomical_preproc['acpc_alignment'][
'acpc_target'] == 'whole-head':
if rpool.check_rpool('space-T2w_desc-brain_mask'):
acpc_blocks_T2 = [
acpc_align_head_with_mask_T2
# outputs space-T2w_desc-brain_mask for later - keep the mask (the user provided)
]
else:
acpc_blocks_T2 = [
acpc_align_head_T2 # does not output nor generate a mask
]
anat_preproc_blocks_T2 = [
registration_T2w_to_T1w,
non_local_means_T2,
n4_bias_correction_T2,
t1t2_bias_correction
]
if cfg.anatomical_preproc['acpc_alignment']['run_before_preproc']:
anat_blocks_T2 = acpc_blocks_T2 + anat_preproc_blocks_T2
else:
anat_blocks_T2 = anat_preproc_blocks_T2 + acpc_blocks_T2
pipeline_blocks += anat_blocks_T2
# Anatomical T1 brain extraction
if not rpool.check_rpool('desc-brain_T1w'):
anat_brain_blocks = [
brain_extraction
]
pipeline_blocks += anat_brain_blocks
# T2 brain masking
if not rpool.check_rpool('space-T2w_desc-brain_mask'):
anat_brain_mask_blocks_T2 = [
brain_mask_T2
]
pipeline_blocks += anat_brain_mask_blocks_T2
if not rpool.check_rpool('desc-brain_T2w'):
anat_brain_blocks_T2 = [
brain_extraction_T2
]
pipeline_blocks += anat_brain_blocks_T2
return pipeline_blocks
def build_T1w_registration_stack(rpool, cfg, pipeline_blocks=None):
if not pipeline_blocks:
pipeline_blocks = []
reg_blocks = []
if not rpool.check_rpool('from-T1w_to-template_mode-image_xfm'):
reg_blocks = [
[register_ANTs_anat_to_template, register_FSL_anat_to_template],
overwrite_transform_anat_to_template,
correct_restore_brain_intensity_abcd # ABCD-options pipeline
]
if cfg.voxel_mirrored_homotopic_connectivity['run']:
if not rpool.check_rpool('from-T1w_to-symtemplate_mode-image_xfm'):
reg_blocks.append([register_symmetric_ANTs_anat_to_template,
register_symmetric_FSL_anat_to_template])
pipeline_blocks += reg_blocks
return pipeline_blocks
def build_segmentation_stack(rpool, cfg, pipeline_blocks=None):
if not pipeline_blocks:
pipeline_blocks = []
if not rpool.check_rpool('label-CSF_mask') or \
not rpool.check_rpool('label-WM_mask'):
seg_blocks = [
[tissue_seg_fsl_fast,
tissue_seg_ants_prior]
#tissue_seg_freesurfer
]
if 'T1_Template' in cfg.segmentation['tissue_segmentation'][
'Template_Based']['template_for_segmentation']:
seg_blocks.append(tissue_seg_T1_template_based)
if 'EPI_Template' in cfg.segmentation['tissue_segmentation'][
'Template_Based']['template_for_segmentation']:
seg_blocks.append(tissue_seg_EPI_template_based)
pipeline_blocks += seg_blocks
return pipeline_blocks
def connect_pipeline(wf, cfg, rpool, pipeline_blocks):
for block in pipeline_blocks:
try:
nb = NodeBlock(block)
wf = nb.connect_block(wf, cfg, rpool)
except LookupError as e: