def eval_1(args):
first_file = open(args[0]).read().splitlines()
#
for line in first_file:
if (line.startswith("pdb_file = ")):
pdb_file = line.split(" ", 2)[2]
assert pdb_file[0] == pdb_file[-1]
assert pdb_file[0] in ['"', "'"]
pdb_file = op.basename(pdb_file[1:-1])
break
else:
raise RuntimeError('pdb_file = "..." not found.')
#
for line in first_file:
if (line ==
"random_displacements_parameterization = *constrained cartesian"
):
random_displacements_parameterization = "constrained"
break
elif (line ==
"random_displacements_parameterization = constrained *cartesian"
):
random_displacements_parameterization = "cartesian"
break
else:
raise RuntimeError(
"random_displacements_parameterization = constrained or cartesian"
" not found.")
#
for line in first_file:
if (line == "algorithm = *minimization annealing"):
algorithm = "minimization"
break
elif (line == "algorithm = minimization *annealing"):
algorithm = "annealing"
break
else:
raise RuntimeError("algorithm = minimization or annealing not found.")
#
del first_file
#
tst_tardy_pdb_master_phil = tst_tardy_pdb.get_master_phil()
tst_tardy_pdb_params = tst_tardy_pdb_master_phil.extract()
if (algorithm == "minimization"):
parameter_trial_table \
= tst_tardy_comprehensive.common_parameter_trial_table
elif (algorithm == "annealing"):
parameter_trial_table \
= tst_tardy_comprehensive.annealing_parameter_trial_table
else:
raise AssertionError
cp_n_trials = tst_tardy_comprehensive.number_of_trials(
table=parameter_trial_table)
#
random_seed_rmsd = []
for cp_i_trial in xrange(cp_n_trials):
random_seed_rmsd.append({})
for file_name in args:
for line in open(file_name).read().splitlines():
if (not line.startswith("RESULT_cp_i_trial_random_seed_rmsd: ")):
continue
flds = line.split(None, 3)
assert len(flds) == 4
cp_i_trial = int(flds[1])
random_seed = int(flds[2])
rmsd = flex.double(eval(flds[3]))
assert not random_seed_rmsd[cp_i_trial].has_key(random_seed)
random_seed_rmsd[cp_i_trial][random_seed] = rmsd
random_seeds_found = dict_with_default_0()
for cp_i_trial in xrange(cp_n_trials):
random_seeds_found[tuple(sorted(
random_seed_rmsd[cp_i_trial].keys()))] += 1
if (len(random_seeds_found) != 1):
print random_seeds_found
raise RuntimeError("Unexpected random_seeds_found (see output).")
assert random_seeds_found.values()[0] == cp_n_trials
random_seeds_found = random_seeds_found.keys()[0]
assert random_seeds_found == tuple(range(len(random_seeds_found)))
rmsds = []
for cp_i_trial in xrange(cp_n_trials):
rmsds.append([
random_seed_rmsd[cp_i_trial][random_seed]
for random_seed in xrange(len(random_seeds_found))
])
#
write_separate_pages = False
if (algorithm == "minimization"):
rmsd_start_final_plots_minimization(
pdb_file=pdb_file,
random_displacements_parameterization=
random_displacements_parameterization,
tst_tardy_pdb_params=tst_tardy_pdb_params,
parameter_trial_table=parameter_trial_table,
cp_n_trials=cp_n_trials,
rmsds=rmsds,
rmsd_n_n=50,
write_separate_pages=write_separate_pages)
elif (algorithm == "annealing"):
rmsd_start_final_plots_annealing(
pdb_file=pdb_file,
random_displacements_parameterization=
random_displacements_parameterization,
tst_tardy_pdb_params=tst_tardy_pdb_params,
parameter_trial_table=parameter_trial_table,
cp_n_trials=cp_n_trials,
rmsds=rmsds,
write_separate_pages=write_separate_pages)
def eval_1(args):
first_file = open(args[0]).read().splitlines()
#
for line in first_file:
if (line.startswith("pdb_file = ")):
pdb_file = line.split(" ", 2)[2]
assert pdb_file[0] == pdb_file[-1]
assert pdb_file[0] in ['"', "'"]
pdb_file = op.basename(pdb_file[1:-1])
break
else:
raise RuntimeError('pdb_file = "..." not found.')
#
for line in first_file:
if (line ==
"random_displacements_parameterization = *constrained cartesian"):
random_displacements_parameterization = "constrained"
break
elif (line ==
"random_displacements_parameterization = constrained *cartesian"):
random_displacements_parameterization = "cartesian"
break
else:
raise RuntimeError(
"random_displacements_parameterization = constrained or cartesian"
" not found.")
#
for line in first_file:
if (line == "algorithm = *minimization annealing"):
algorithm = "minimization"
break
elif (line == "algorithm = minimization *annealing"):
algorithm = "annealing"
break
else:
raise RuntimeError("algorithm = minimization or annealing not found.")
#
del first_file
#
tst_tardy_pdb_master_phil = tst_tardy_pdb.get_master_phil()
tst_tardy_pdb_params = tst_tardy_pdb_master_phil.extract()
if (algorithm == "minimization"):
parameter_trial_table \
= tst_tardy_comprehensive.common_parameter_trial_table
elif (algorithm == "annealing"):
parameter_trial_table \
= tst_tardy_comprehensive.annealing_parameter_trial_table
else:
raise AssertionError
cp_n_trials = tst_tardy_comprehensive.number_of_trials(
table=parameter_trial_table)
#
random_seed_rmsd = []
for cp_i_trial in xrange(cp_n_trials):
random_seed_rmsd.append({})
for file_name in args:
for line in open(file_name).read().splitlines():
if (not line.startswith("RESULT_cp_i_trial_random_seed_rmsd: ")):
continue
flds = line.split(None, 3)
assert len(flds) == 4
cp_i_trial = int(flds[1])
random_seed = int(flds[2])
rmsd = flex.double(eval(flds[3]))
assert not random_seed_rmsd[cp_i_trial].has_key(random_seed)
random_seed_rmsd[cp_i_trial][random_seed] = rmsd
random_seeds_found = dict_with_default_0()
for cp_i_trial in xrange(cp_n_trials):
random_seeds_found[tuple(sorted(random_seed_rmsd[cp_i_trial].keys()))] += 1
if (len(random_seeds_found) != 1):
print random_seeds_found
raise RuntimeError("Unexpected random_seeds_found (see output).")
assert random_seeds_found.values()[0] == cp_n_trials
random_seeds_found = random_seeds_found.keys()[0]
assert random_seeds_found == tuple(range(len(random_seeds_found)))
rmsds = []
for cp_i_trial in xrange(cp_n_trials):
rmsds.append([random_seed_rmsd[cp_i_trial][random_seed]
for random_seed in xrange(len(random_seeds_found))])
#
write_separate_pages = False
if (algorithm == "minimization"):
rmsd_start_final_plots_minimization(
pdb_file=pdb_file,
random_displacements_parameterization
=random_displacements_parameterization,
tst_tardy_pdb_params=tst_tardy_pdb_params,
parameter_trial_table=parameter_trial_table,
cp_n_trials=cp_n_trials,
rmsds=rmsds,
rmsd_n_n=50,
write_separate_pages=write_separate_pages)
elif (algorithm == "annealing"):
rmsd_start_final_plots_annealing(
pdb_file=pdb_file,
random_displacements_parameterization
=random_displacements_parameterization,
tst_tardy_pdb_params=tst_tardy_pdb_params,
parameter_trial_table=parameter_trial_table,
cp_n_trials=cp_n_trials,
rmsds=rmsds,
write_separate_pages=write_separate_pages)
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