def run(args): if (len(args) == 0): usage() for arg in args: if (arg not in ["n", "i"]): usage() cm = chunk_manager(n=1, i=0).queuing_system_overrides_chunk() for arg in args: if (arg == "n"): print cm.n, elif (arg == "i"): print cm.i, print
def chunk_manager(self): result = chunk_manager(n=self.n, i=self.i) if (self.easy_all): result.easy_all() return result
def run(args): local_master_phil = get_master_phil() argument_interpreter = local_master_phil.command_line_argument_interpreter( ) phil_objects = [] for arg in args: phil_objects.append(argument_interpreter.process(arg=arg)) local_params = local_master_phil.fetch(sources=phil_objects).extract() chunk = chunk_manager(n=local_params.chunk[0], i=local_params.chunk[1]).easy_all() local_master_phil.format(local_params).show() print # assert local_params.pdb_file is not None assert op.isfile(local_params.pdb_file) # tst_tardy_pdb_master_phil = tst_tardy_pdb.get_master_phil() tst_tardy_pdb_params = tst_tardy_pdb_master_phil.extract() tst_tardy_pdb_params.tardy_displacements = Auto tst_tardy_pdb_params.tardy_displacements_auto.parameterization \ = local_params.random_displacements_parameterization if (local_params.algorithm == "minimization"): parameter_trial_table = common_parameter_trial_table elif (local_params.algorithm == "annealing"): parameter_trial_table = annealing_parameter_trial_table else: raise AssertionError cp_n_trials = number_of_trials(table=parameter_trial_table) print "Number of parameter trials:", cp_n_trials print "parameter_trial_table:" pprint.pprint(parameter_trial_table) print # show_times_at_exit() # params_shown_once_already = False tst_tardy_pdb_log_shown_once_already = False for cp_i_trial in xrange(cp_n_trials): if (chunk.skip_iteration(i=cp_i_trial)): continue print "cp_i_trial: %d / %d = %.2f %%" % (cp_i_trial, cp_n_trials, 100 * (cp_i_trial + 1) / cp_n_trials) if (local_params.verbose): print sys.stdout.flush() set_parameters(params=tst_tardy_pdb_params, trial_table=parameter_trial_table, cp_i_trial=cp_i_trial) if (local_params.algorithm == "minimization"): if (local_params.orca_experiments): tst_tardy_pdb_params.keep_all_restraints = True if (tst_tardy_pdb_params.emulate_cartesian): tst_tardy_pdb_params.orca_experiments = False else: tst_tardy_pdb_params.orca_experiments = True tst_tardy_pdb_params.number_of_cooling_steps = 0 tst_tardy_pdb_params.minimization_max_iterations = None elif (local_params.algorithm == "annealing"): tst_tardy_pdb_params.number_of_time_steps = 1 tst_tardy_pdb_params.time_step_pico_seconds = 0.001 tst_tardy_pdb_params.minimization_max_iterations = 0 else: raise AssertionError for random_seed in xrange(local_params.number_of_random_trials): tst_tardy_pdb_params.random_seed = random_seed tst_tardy_pdb_params.dihedral_function_type \ = local_params.dihedral_function_type if (local_params.verbose or not params_shown_once_already): params_shown_once_already = True tst_tardy_pdb_master_phil.format(tst_tardy_pdb_params).show() print sys.stdout.flush() if (local_params.hot): if (local_params.verbose): tst_tardy_pdb_log = sys.stdout else: tst_tardy_pdb_log = StringIO() coll = collector() try: tst_tardy_pdb.run_test(params=tst_tardy_pdb_params, pdb_files=[local_params.pdb_file], other_files=[], callback=coll, log=tst_tardy_pdb_log) except KeyboardInterrupt: raise except Exception: print print "tst_tardy_pdb_params leading to exception:" print tst_tardy_pdb_master_phil.format( tst_tardy_pdb_params).show() print if (not local_params.verbose): sys.stdout.write(tst_tardy_pdb_log.getvalue()) sys.stdout.flush() if (not local_params.keep_going): raise report_exception( context_info="cp_i_trial=%d, random_seed=%d" % (cp_i_trial, random_seed)) else: if (not local_params.verbose and not tst_tardy_pdb_log_shown_once_already): tst_tardy_pdb_log_shown_once_already = True sys.stdout.write(tst_tardy_pdb_log.getvalue()) print "RESULT_cp_i_trial_random_seed_rmsd:", \ cp_i_trial, random_seed, list(coll.rmsd) sys.stdout.flush() first_pass = False if (local_params.hot): print
def run(args): local_master_phil = get_master_phil() argument_interpreter = local_master_phil.command_line_argument_interpreter() phil_objects = [] for arg in args: phil_objects.append(argument_interpreter.process(arg=arg)) local_params = local_master_phil.fetch(sources=phil_objects).extract() chunk = chunk_manager( n=local_params.chunk[0], i=local_params.chunk[1]).easy_all() local_master_phil.format(local_params).show() print # assert local_params.pdb_file is not None assert op.isfile(local_params.pdb_file) # tst_tardy_pdb_master_phil = tst_tardy_pdb.get_master_phil() tst_tardy_pdb_params = tst_tardy_pdb_master_phil.extract() tst_tardy_pdb_params.tardy_displacements = Auto tst_tardy_pdb_params.tardy_displacements_auto.parameterization \ = local_params.random_displacements_parameterization if (local_params.algorithm == "minimization"): parameter_trial_table = common_parameter_trial_table elif (local_params.algorithm == "annealing"): parameter_trial_table = annealing_parameter_trial_table else: raise AssertionError cp_n_trials = number_of_trials(table=parameter_trial_table) print "Number of parameter trials:", cp_n_trials print "parameter_trial_table:" pprint.pprint(parameter_trial_table) print # show_times_at_exit() # params_shown_once_already = False tst_tardy_pdb_log_shown_once_already = False for cp_i_trial in xrange(cp_n_trials): if (chunk.skip_iteration(i=cp_i_trial)): continue print "cp_i_trial: %d / %d = %.2f %%" % ( cp_i_trial, cp_n_trials, 100 * (cp_i_trial+1) / cp_n_trials) if (local_params.verbose): print sys.stdout.flush() set_parameters( params=tst_tardy_pdb_params, trial_table=parameter_trial_table, cp_i_trial=cp_i_trial) if (local_params.algorithm == "minimization"): if (local_params.orca_experiments): tst_tardy_pdb_params.keep_all_restraints = True if (tst_tardy_pdb_params.emulate_cartesian): tst_tardy_pdb_params.orca_experiments = False else: tst_tardy_pdb_params.orca_experiments = True tst_tardy_pdb_params.number_of_cooling_steps = 0 tst_tardy_pdb_params.minimization_max_iterations = None elif (local_params.algorithm == "annealing"): tst_tardy_pdb_params.number_of_time_steps = 1 tst_tardy_pdb_params.time_step_pico_seconds = 0.001 tst_tardy_pdb_params.minimization_max_iterations = 0 else: raise AssertionError for random_seed in xrange(local_params.number_of_random_trials): tst_tardy_pdb_params.random_seed = random_seed tst_tardy_pdb_params.dihedral_function_type \ = local_params.dihedral_function_type if (local_params.verbose or not params_shown_once_already): params_shown_once_already = True tst_tardy_pdb_master_phil.format(tst_tardy_pdb_params).show() print sys.stdout.flush() if (local_params.hot): if (local_params.verbose): tst_tardy_pdb_log = sys.stdout else: tst_tardy_pdb_log = StringIO() coll = collector() try: tst_tardy_pdb.run_test( params=tst_tardy_pdb_params, pdb_files=[local_params.pdb_file], other_files=[], callback=coll, log=tst_tardy_pdb_log) except KeyboardInterrupt: raise except Exception: print print "tst_tardy_pdb_params leading to exception:" print tst_tardy_pdb_master_phil.format(tst_tardy_pdb_params).show() print if (not local_params.verbose): sys.stdout.write(tst_tardy_pdb_log.getvalue()) sys.stdout.flush() if (not local_params.keep_going): raise report_exception( context_info="cp_i_trial=%d, random_seed=%d" % ( cp_i_trial, random_seed)) else: if ( not local_params.verbose and not tst_tardy_pdb_log_shown_once_already): tst_tardy_pdb_log_shown_once_already = True sys.stdout.write(tst_tardy_pdb_log.getvalue()) print "RESULT_cp_i_trial_random_seed_rmsd:", \ cp_i_trial, random_seed, list(coll.rmsd) sys.stdout.flush() first_pass = False if (local_params.hot): print