cg_annealer = cg_pyrosetta.CG_monte_carlo.CGMonteCarloAnnealer(
seq_mover=sequence_mover,
score_function=energy_function,
pose=pose,
param_file_object=annealer_params)
# Setup Configuration/Energy observer for saving minimum energy structures
struct_obs = cg_pyrosetta.CG_monte_carlo.StructureObserver(
cg_annealer.get_mc_sim())
energy_obs = cg_pyrosetta.CG_monte_carlo.EnergyObserver(
cg_annealer.get_mc_sim())
cg_annealer.registerObserver(struct_obs)
cg_annealer.registerObserver(energy_obs)
# Run Annealer
cg_annealer.run_schedule()
min_pose = cg_annealer._cg_mc_sim.get_minimum_energy_pose()
print("Writing structure to:")
print(job.fn("minimum.pdb"))
min_pose.dump_pdb(job.fn("minimum.pdb"))
end = time.time()
t_rep = end - start
print(t_rep, "seconds")
job.data['timing'] = t_rep
if __name__ == '__main__':
FlowProject().main()
def main():
global task_args, other_args, modelPathObj
assert all(map(lambda x: x is not None, [
model_path, modelScript, expCode, workspaceRoot, expProjectName
]))
modelPathObj = Path(__file__).parent / model_path
task_parser = argparse.ArgumentParser(add_help=False)
task_parser.add_argument("--config", "-c", default=None)
task_parser.add_argument("--exp_regex", default=None)
task_parser.add_argument("--arg_regex", default=None)
task_parser.add_argument("--model_args", default="", type=str)
task_parser.add_argument("--clear_workspace", action="store_true")
task_parser.add_argument("--clean_workspace", action="store_true")
if ("run" in sys.argv[1:]) or ("exec" in sys.argv[1:]) or ("--help" in sys.argv[1:]):
task_parser.add_argument("--tuning", action="store_true")
task_parser.add_argument("--use_env", default=None)
task_parser.add_argument("--interactive", "-i", action="store_true")
task_parser.add_argument("--exp_tags", nargs="+", default=[])
task_parser.add_argument("--check_paths", action="store_true")
task_parser.add_argument("--export_dataset", default=None)
extra_arguments_func(task_parser)
task_args, other_args = task_parser.parse_known_args()
if not hasattr(task_args, "model_args"):
task_args.model_args = ""
task_args.split_filter = None
task_args.split_doc_filter = None
if task_args.config:
with open(task_args.config, "r") as args_in:
argsDict = json.load(args_in)
if "model_args" in argsDict:
if task_args.model_args:
task_args.model_args = [
(arg + " " + task_args.model_args) for arg in argsDict["model_args"]]
else:
task_args.model_args = argsDict["model_args"]
if "exp_regex" in argsDict:
task_args.exp_regex = argsDict["exp_regex"]
if "arg_regex" in argsDict:
task_args.arg_regex = argsDict["arg_regex"]
if "exp_tags" in argsDict:
task_args.exp_tags = argsDict["exp_tags"]
if "split_filter" in argsDict:
task_args.split_filter = argsDict["split_filter"]
if "split_doc_filter" in argsDict:
task_args.split_doc_filter = argsDict["split_doc_filter"]
if ("-f" not in other_args) and ("-j" not in other_args):
if "graph_filter_dict" in argsDict:
graph_filter_dict = argsDict["graph_filter_dict"]
sys.argv += ["-f", json.dumps(graph_filter_dict)]
print(sys.argv)
elif "graph_filter" in argsDict:
graph_filter = argsDict["graph_filter"]
sys.argv += ["-f"] + graph_filter
print(sys.argv)
if task_args.clean_workspace:
sys.argv += ["-o", "clean_workspace"]
if task_args.clear_workspace:
sys.argv += ["-o", "clear_workspace"]
if task_args.model_args and type(task_args.model_args) is str:
task_args.model_args = task_args.model_args.split(";")
print("Model args: {}".format(task_args.model_args))
if not (modelPathObj.exists() and modelPathObj.is_dir()):
raise ValueError("Path {} does not exist or is not a folder. \n"
"Please change the 'model_path' variable in the script.".format(model_path))
if ("--debug" in other_args) or ("--show-traceback" in other_args) or vars(task_args).get("tuning", False):
flags.log_to_terminal = True
print("Log to terminal enabled.")
if ("status" in sys.argv[1:]):
while sys.argv[1] != "status":
del sys.argv[1]
FlowProject().main()
else:
task_help_parser = argparse.ArgumentParser(parents=[task_parser])
FlowProject().main(parser=task_help_parser)
if task_args.model_args:
print("Model args: {}".format(task_args.model_args))