def CreateJobs(self):
"""
+ Create...
"""
+
num_splits = self.global_settings['num_splits']
- run_dir = '/fml/ag-raetsch/home/fabio/tmp/newest_run/alignment/run_enable_quality_scores_+_enable_splice_signals_+_enable_intron_length_+'
- param_fname = jp(run_dir,'param_526.pickle')
- # param_fname = self.global_settings['prediction_parameter_fn']
- run_fname = jp(run_dir,'run_obj.pickle')
- # run_fname = self.global_settings['run_fn']
+ #run_dir = '/fml/ag-raetsch/home/fabio/tmp/newest_run/alignment/run_enable_quality_scores_+_enable_splice_signals_+_enable_intron_length_+'
+ #param_fname = jp(run_dir,'param_526.pickle')
+ param_fname = self.global_settings['prediction_parameter_fn']
+ #run_fname = jp(run_dir,'run_obj.pickle')
+ run_fname = self.global_settings['run_fn']
#result_dir = '/fml/ag-raetsch/home/fabio/tmp/vmatch_evaluation/main'
result_dir = self.global_settings['approximation_dir']
import pdb
import sys
-from optparse import OptionParser
-
from qpalma.gridtools import ApproximationTask,PreprocessingTask
from qpalma.gridtools import AlignmentTask,PostprocessingTask
-
-Errormsg = """Usage is: python qpalma_pipeline.py <config filename>"""
-
-
-"""
-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
-"""
-
-jp = os.path.join
-
-def parseSettings(filename):
- """
- """
-
- #global_settings = {\
- #'result_dir':'/fml/ag-raetsch/...',\
- #'read_ascii_data_fn':'/fml/ag-raetsch/...',\
- #'num_splits':50
- #'global_log_fn':'~/qpalma.log'
- #}
-
- global_settings = {}
-
- for line in open(filename):
- if not line.strip() or line.startswith('#'):
- continue
-
- key,val = line.strip().replace(' ','').split('=')
- global_settings[key] = val
-
- return global_settings
-
-
-def makeSettings(global_settings):
- """
-
- """
-
- # first check wether the top level result directory exists
- assert os.path.exists(global_settings['result_dir']), 'Error: You have to specify a existing result directory!'
-
- result_dir = global_settings['result_dir']
-
- # now create some subdirectories needed for the different steps performed by QPalma
- global_settings['approximation_dir'] = jp(result_dir,'approximation')
- global_settings['preproc_dir'] = jp(result_dir,'preprocessing')
- global_settings['postproc_dir'] = jp(result_dir,'postprocessing')
- global_settings['prediction_dir'] = jp(result_dir,'prediction')
- global_settings['training_dir'] = jp(result_dir,'training')
-
- for dir_name in ['approximation_dir', 'preproc_dir', 'postproc_dir', 'prediction_dir', 'training_dir']:
- try:
- os.mkdir(global_settings[dir_name])
- except:
- print 'Error: There was a problem generating the subdirectory: %s' % dir_name
-
- try:
- os.mkdir(global_settings['global_log_fn'])
- except:
- print 'Error: There was a problem generating the logfile %s' % global_settings['global_log_fn']
-
- return global_settings
-
-
-def checkSettings(global_settings):
- for key,val in global_settings.items():
- if key.endswith('_fn'):
- assert os.path.exists(val), 'Error: Path/File %s with value %s does not seem to exist!' % (key,val)
-
-
- if key.endswith('_dir'):
- assert os.path.exists(val), 'Error: Path/File %s with value %s does not seem to exist!' % (key,val)
-
-
- return True
+from qpalma.settingsParser import parseSettings
+Errormsg = """Usage is: python qpalma_pipeline.py <config filename>"""
class System:
all parameters.
"""
- #parser = create_option_parser()
- #(options, args) = parser.parse_args()
-
- global_settings = parseSettings(filename)
- global_settings = makeSettings(global_settings)
- assert checkSettings(global_settings), 'Check your settings some entries were invalid!'
-
- self.global_settings = global_settings
-
+ self.global_settings = parseSettings(filename)
pdb.set_trace()
+
def training(self):
"""
This function is responsible for the whole training process. It first
# 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()
filename = sys.argv[1]
assert os.path.exists(filename), Errormsg
system_obj = System(filename)
- #system_obj.prediction()
+ system_obj.prediction()
+
#system_obj.training()