from createAlignmentFileFromPrediction import create_alignment_file
+from PipelineHeuristic import *
+
import gridtools
from utils import get_slices,split_file
After creation of jobs this function submits them to the cluster.
"""
for current_job in self.functionJobs:
- self.sid, self.jobids = submit_jobs([functionJobs])
+ self.sid, self.jobids = submit_jobs([current_job])
def Restart(self,id):
while not get_status(self.sid, self.jobids):
time.sleep(7)
print 'collecting jobs'
- retjobs = collect_jobs(sid, jobids, myjobs)
- print "ret fields AFTER execution on cluster"
- for (i, job) in enumerate(retjobs):
- print "Job #", i, "- ret: ", job.ret
+ retjobs = collect_jobs(self.sid, self.jobids, self.functionJobs)
- print '--------------'
+ #print "ret fields AFTER execution on cluster"
+ #for (i, job) in enumerate(retjobs):
+ # print "Job #", i, "- ret: ", job.ret
+ #print '--------------'
data_fname = jp(result_dir,'map.part_%d'%idx)
result_fname = jp(result_dir,'map.vm.part_%d.heuristic'%idx)
- current_job = KybJob(gridtools.TaskStarter,[run_fname,data_fname,param_fname,result_fname])
+ current_job = KybJob(gridtools.ApproximationTaskStarter,[run_fname,data_fname,param_fname,result_fname])
current_job.h_vmem = '25.0G'
#current_job.express = 'True'
self.functionJobs.append(current_job)
- def TaskStarter(run_fname,data_fname,param_fname,result_fname):
- ph1 = PipelineHeuristic(run_fname,data_fname,param_fname,result_fname)
- ph1.filter()
+def ApproximationTaskStarter(run_fname,data_fname,param_fname,result_fname):
+ ph1 = PipelineHeuristic(run_fname,data_fname,param_fname,result_fname)
+ ph1.filter()
- return 'finished filtering set %s.' % data_fname
+ return 'finished filtering set %s.' % data_fname
class PreprocessingTask(ClusterTask):
# After filtering combine the filtered matches from the first run and the
# found matches from the second run to a full dataset
- sys.exit(0)
-
pre_task = PreprocessingTask(self.global_settings)
- pre_task.createJobs()
- pre_task.submit()
- pre_task.checkIfTaskFinished()
+ pre_task.CreateJobs()
+ pre_task.Submit()
+ pre_task.CheckIfTaskFinished()
+
+ sys.exit(0)
# Now that we have a dataset we can perform the accurate alignments for this
# data
align_task = AlignmentTask(self.global_settings)
- align_task.createJobs()
- align_task.submit()
- align_task.checkIfTaskFinished()
+ align_task.CreateJobs()
+ align_task.Submit()
+ align_task.CheckIfTaskFinished()
# The results of the above alignment step can be converted to a data format
# needed for further postprocessing.
post_task = PostprocessingTask(self.global_settings)
- post_task.createJobs()
- post_task.submit()
- post_task.checkIfTaskFinished()
+ post_task.CreateJobs()
+ post_task.Submit()
+ post_task.CheckIfTaskFinished()
print "Success!"