--- /dev/null
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import cPickle
+import math
+import os
+import os.path
+import pdb
+import sys
+import time
+
+from threading import Thread
+
+from pythongrid import KybJob, Usage
+from pythongrid import process_jobs, submit_jobs, collect_jobs, get_status
+
+from createAlignmentFileFromPrediction import create_alignment_file
+
+import gridtools
+
+jp = os.path.join
+
+
+class ClusterTask(Thread):
+ """
+ This class..
+ """
+
+ def __init__(self):
+ self.sleep_time = 0
+
+ # this list stores the cluster/local jobs objects
+ self.functionJobs = []
+
+
+ def createJobs(self):
+ pass
+
+
+ def submit(self):
+ for current_job in self.functionJobs:
+ (sid, jobids) = submit_jobs([functionJobs])
+ time.sleep(self.sleep_time)
+
+
+ def checkIfTaskFinished(self):
+ pass
+
+
+
+class ApproximationTask(ClusterTask):
+ """
+ """
+
+ def __init__(self):
+ ClusterTask.__init__(self)
+
+
+ def g_heuristic(run_fname,data_fname,param_fname,result_fname):
+ #print 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
+
+ def createJobs(self):
+ num_splits = 25
+
+ run_dir = '/fml/ag-raetsch/home/fabio/tmp/newest_run/alignment/run_enable_quality_scores_+_enable_splice_signals_+_enable_intron_length_+'
+ #data_dir = '/fml/ag-raetsch/home/fabio/tmp/lyrata_analysis/'
+
+ data_dir = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/main'
+
+ run_fname = jp(run_dir,'run_obj.pickle')
+
+ #original_map_fname = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/main/map.vm'
+ #split_file(original_map_fname,data_dir,num_splits)
+
+ param_fname = jp(run_dir,'param_526.pickle')
+
+ functionJobs=[]
+
+ for idx in range(0,num_splits):
+ data_fname = jp(data_dir,'map.part_%d'%idx)
+ result_fname = jp(data_dir,'map.vm.part_%d.heuristic'%idx)
+
+ current_job = KybJob(grid_heuristic.g_heuristic,[run_fname,data_fname,param_fname,result_fname])
+ current_job.h_vmem = '25.0G'
+ #current_job.express = 'True'
+
+ print "job #1: ", current_job.nativeSpecification
+
+ self.functionJobs.append(current_job)
+
+
+
+
+class PreprocessingTask(ClusterTask):
+ """
+ This class encapsules some...
+ """
+
+ def __init__(self):
+ ClusterTask.__init__(self)
+
+
+class AlignmentTask(ClusterTask):
+
+ def __init__(self):
+ ClusterTask.__init__(self)
+
+
+ def get_slices(dataset_size,num_nodes):
+ all_instances = []
+
+ part = dataset_size / num_nodes
+ begin = 0
+ end = 0
+ for idx in range(1,num_nodes+1):
+
+ if idx == num_nodes:
+ begin = end
+ end = dataset_size
+ else:
+ begin = end
+ end = begin+part
+
+ params = (begin,end)
+
+ all_instances.append(params)
+
+ return all_instances
+
+
+ def makeJobs(run,dataset_fn,chunks,param):
+ """
+ """
+
+ jobs=[]
+
+ for c_name,current_chunk in chunks:
+ current_job = KybJob(grid_predict.g_predict,[run,dataset_fn,current_chunk,param,c_name])
+ current_job.h_vmem = '20.0G'
+ #current_job.express = 'True'
+
+ print "job #1: ", current_job.nativeSpecification
+
+ jobs.append(current_job)
+
+ return jobs
+
+
+ def create_and_submit():
+ """
+
+ """
+
+ jp = os.path.join
+
+ run_dir = '/fml/ag-raetsch/home/fabio/tmp/newest_run/alignment/saved_run'
+
+ run = cPickle.load(open(jp(run_dir,'run_obj.pickle')))
+ run['name'] = 'saved_run'
+
+ param = cPickle.load(open(jp(run_dir,'param_526.pickle')))
+
+ run['result_dir'] = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/spliced_1/prediction'
+ dataset_fn = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/spliced_1/dataset/dataset_run_1.pickle.pickle'
+ prediction_keys_fn = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/spliced_1/dataset/dataset_run_1.pickle.keys.pickle'
+
+ prediction_keys = cPickle.load(open(prediction_keys_fn))
+
+ print 'Found %d keys for prediction.' % len(prediction_keys)
+
+ num_splits = 25
+ #num_splits = 1
+ slices = get_slices(len(prediction_keys),num_splits)
+ chunks = []
+ for idx,slice in enumerate(slices):
+ #if idx != 0:
+ c_name = 'chunk_%d' % idx
+ chunks.append((c_name,prediction_keys[slice[0]:slice[1]]))
+
+ self.functionJobs = makeJobs(run,dataset_fn,chunks,param)
+
+ sum = 0
+ for size in [len(elem) for name,elem in chunks]:
+ sum += size
+
+ print 'Got %d job(s)' % len(functionJobs)
+
+
+ def g_predict(run,dataset_fn,prediction_keys,param,set_name):
+ """
+
+ """
+
+ qp = QPalma()
+ qp.predict(run,dataset_fn,prediction_keys,param,set_name)
+
+ return 'finished prediction of set %s.' % set_name
+
+
+
+class TrainingTask(ClusterTask):
+
+ def __init__(self):
+ ClusterTask.__init__(self)
+
+
+class PostprocessingTask(ClusterTask):
+ """
+
+ """
+
+ def __init__(self):
+ ClusterTask.__init__(self)
+
+
+ def g_alignment(chunk_fn,result_fn):
+ create_alignment_file(chunk_fn,result_fn)
+
+
+ def createJobs(self):
+ run_dir = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/spliced_1/prediction'
+ result_dir = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/spliced_1/alignment'
+
+ chunks_fn = []
+ for fn in os.listdir(run_dir):
+ if fn.startswith('chunk'):
+ chunks_fn.append(fn)
+
+ print chunks_fn
+
+ functionJobs=[]
+
+ for chunk_fn in chunks_fn:
+ chunk_name = chunk_fn[:chunk_fn.find('.')]
+ result_fn = jp(result_dir,'%s.align_remap'%chunk_name)
+ chunk_fn = jp(run_dir,chunk_fn)
+
+ current_job = KybJob(grid_alignment.g_alignment,[chunk_fn,result_fn])
+ current_job.h_vmem = '15.0G'
+ current_job.express = 'True'
+
+ print "job #1: ", current_job.nativeSpecification
--- /dev/null
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+#
+# This file contains the main interface to the QPalma pipeline.
+#
+#
+#
+
+
+from optparse import OptionParser
+
+
+from qpalma.gridtools import *
+
+
+def create_option_parser():
+ parser = OptionParser()
+
+ #
+ parser.add_option("-ci", "--check_and_init", help="check configuration and initialize directories")
+
+ #
+ parser.add_option("-r", "--run", help="write report to FILE", metavar="FILE")
+
+ #
+ parser.add_option("-xx", "--clear", action="store_false", dest="verbose", help="cleanup directories delete all created data")
+
+ return parser
+
+
+
+
+class System:
+ """
+ This class wraps the outer loop of the qpalma project
+
+ It is responsible for:
+
+ - loading and checking the config file(s)
+ - setting up the different pipeline modules
+ - run the experiment and report the results
+
+
+ """
+
+ # Before creating a candidate spliced read dataset we have to first filter
+ # the matches from the first seed finding run.
+
+ grid_heuristic()
+
+ # approx_task = ApproximationTask(...)
+ # approx_task.createJobs()
+ # approx_task.submit()
+ # approx_task.checkIfTaskFinished()
+
+ # After filtering combine the filtered matches from the first run and the
+ # found matches from the second run to a full dataset
+
+ createNewDataset
+
+ # pre_task = PreprocessingTask(...)
+ # pre_task.createJobs()
+ # pre_task.submit()
+
+ # Now that we have a dataset we can perform the accurate alignments for this
+ # data
+
+ grid_predict()
+
+ # align_task = AlignmentTask(...)
+ # align_task.createJobs()
+ # align_task.submit()
+
+ # The results of the above alignment step can be converted to a data format
+ # needed for further postprocessing
+
+ grid_alignment()
+
+ # post_task = PostprocessingTask(...)
+ # post_task.createJobs()
+ # post_task.submit()
+
+
+if __name__ == '__main__':
+ parser = create_option_parser()
+ (options, args) = parser.parse_args()
projects / qpalma.git / commitdiff