def generate_dax(self, daxfile):
from Pegasus.DAX3 import ADAG, Job, File, Link
# The DAX generator
dax = ADAG("pipeline")
# Some bits of metadata. Shoulf put plenty more here.
dax.metadata("owner", self.pipeline.owner)
dax.metadata("basename", self.pipeline.basename)
dax.metadata("version", self.pipeline.version)
# string tag -> pegasus File object mapping of all the
# inputs and outputs used by any pipeline stage.
files = {}
# First generate the overall inputs to the pipeline,
# i.e. ones that are not generated by any other stage
# but must be specified at the start
for tag in self.pipeline.input_tags():
path = self.info['inputs'].get(tag)
files[tag] = File(path)
# Now go through the pipeline in sequence.
for stage_name, stage_class in self.pipeline.sequence():
# The stage in the pipeline. We describe the meaning of it
# (which image it corresponds to)
# in the transformation catalog generation
job = Job(stage_name, id=stage_name)
# Configuration files for this job.
# These will not be built during the pipeline and must be
# provided by the user
for config_tag, config_filename in stage_class.config.items():
filename = self.pipeline.cfg[stage_name]['config'][config_tag]
config_path = os.path.join(self.config_dir(), filename)
config = File(config_path)
job.uses(config, link=Link.INPUT)
# Input files for the job, either created by the user or by previous
# stages. In either case they should be in the "files" dictionary, because
# precursor jobs will have been added before this one.
for input_tag in stage_class.inputs.keys():
job.uses(files[input_tag], link=Link.INPUT)
# Output files from the job. These will be created by the job
# and used by future jobs
for output_tag, output_type in stage_class.outputs.items():
output_filename = "{}.{}".format(output_tag, output_type)
output = File(output_filename)
job.uses(output,
link=Link.OUTPUT,
transfer=True,
register=True)
files[output_tag] = output
# Add this job to the pipeline
dax.addJob(job)
# Tell pegasus which jobs this one depends on.
# The pipeline already knows this information.
# The pipeline.sequence command runs through
# the jobs in an order that guarantees that a job's predecessors are
# always done before it is, so they will always exist in the dax by this point.
for predecessor_name in self.pipeline.dependencies(stage_name):
dax.depends(stage_name, predecessor_name)
# Generate the final DAX XML file.
dax.writeXML(open(daxfile, "w"))
from tvb.recon.dax.seeg_computation import SEEGComputation
from tvb.recon.dax.seeg_gain_computation import SeegGainComputation
from tvb.recon.dax.sensor_model import SensorModel
from tvb.recon.dax.source_model import SourceModel
from tvb.recon.dax.t1_processing import T1Processing
from tvb.recon.dax.tracts_generation import TractsGeneration
if __name__ == "__main__":
if len(sys.argv) != 3:
sys.stderr.write("Usage: %s DAXFILE\n" % (sys.argv[0]))
sys.exit(1)
daxfile = sys.argv[1]
patient_file = sys.argv[2]
dax = ADAG("TVB-PIPELINE")
dax.metadata("created", time.ctime())
config = Configuration(patient_file)
subject = config.props[ConfigKey.SUBJECT]
trg_subject = config.props[ConfigKey.TRGSUBJECT]
atlas_suffix = AtlasSuffix.DEFAULT
if config.props[ConfigKey.ATLAS] == Atlas.A2009S:
atlas_suffix = AtlasSuffix.A2009S
t1_processing = T1Processing(subject, config.props[ConfigKey.T1_FRMT], config.props[ConfigKey.T2_FLAG],
config.props[ConfigKey.T2_FRMT], config.props[ConfigKey.FLAIR_FLAG],
config.props[ConfigKey.FLAIR_FRMT], config.props[ConfigKey.OPENMP_THRDS],
atlas_suffix)
from tvb.recon.dax.seeg_computation import SEEGComputation
from tvb.recon.dax.seeg_gain_computation import SeegGainComputation
from tvb.recon.dax.sensor_model import SensorModel
from tvb.recon.dax.source_model import SourceModel
from tvb.recon.dax.t1_processing import T1Processing
from tvb.recon.dax.tracts_generation import TractsGeneration
if __name__ == "__main__":
if len(sys.argv) != 3:
sys.stderr.write("Usage: %s DAXFILE\n" % (sys.argv[0]))
sys.exit(1)
daxfile = sys.argv[1]
patient_file = sys.argv[2]
dax = ADAG("TVB-PIPELINE")
dax.metadata("created", time.ctime())
config = Configuration(patient_file)
subject = config.props[ConfigKey.SUBJECT]
trg_subject = config.props[ConfigKey.TRGSUBJECT]
atlas_suffix = AtlasSuffix.DEFAULT
if config.props[ConfigKey.ATLAS] == Atlas.A2009S:
atlas_suffix = AtlasSuffix.A2009S
t1_processing = T1Processing(
subject, config.props[ConfigKey.T1_FRMT],
config.props[ConfigKey.T2_FLAG], config.props[ConfigKey.T2_FRMT],
config.props[ConfigKey.FLAIR_FLAG], config.props[ConfigKey.FLAIR_FRMT],
#!/usr/bin/env python
import time
import argparse
from Pegasus.DAX3 import ADAG
from bnm.recon.pegasus.config import Configuration
from bnm.recon.pegasus.flirt import step_coregister_t1_dwi
from bnm.recon.pegasus.t1 import steps_recon_all
from bnm.recon.pegasus.diffusion import steps_dwi_preproc
from bnm.recon.pegasus.utils import write_dax
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate a BNM flow")
parser.add_argument("patient_file")
args = parser.parse_args()
dax = ADAG("BNM")
dax.metadata("name", "Brain Network Model Reconstruction WorkFlow")
dax.metadata("created-at", time.ctime())
dax.metadata("flow-configuration", args.patient_file)
config = Configuration(args.patient_file)
relevant_t1_job = steps_recon_all(dax, config)
relevant_dwi_job = steps_dwi_preproc(dax, config.diffusion)
step_coregister_t1_dwi(dax, config, relevant_t1_job, relevant_dwi_job)
write_dax(dax, config.main_dax_path)
def _init_job_graph(self) -> ADAG:
ret = ADAG(self.name)
ret.metadata("name", self.name)
ret.metadata("createdby", self.created_by)
return ret
