def main():
convert_to_position = True
config_data = load_human_config_json()
# data = load_human_data_from_mat(config_data["HUMAN_MAT_DATA_PATH")
data = load_subjects_from_dir(config_data["HUMAN_JSON_DATA_PATH"],
convert_to_position=convert_to_position)
solutions = extract_solution_chains(data)
# save_human_data_pickle(data, solutions, HUMAN_PICKLE_DATA_PATH)
# solutions2 = load_solutions_by_trial(HUMAN_PICKLE_DATA_PATH)
return data
# PICK ONE and comment others
params = PARAMS['CE3-CE4']
# params = PARAMS['CE3-CC4']
# params = PARAMS['CC3-CE4']
# params = PARAMS['CC3-CC4']
# params = PARAMS['CE4']
# params = PARAMS["CC4"]
else:
setting = sys.argv[1]
# pass a string or an index
try:
params = PARAMS[IDX_TO_PARAMS[int(setting) - 1]]
except Exception:
params = PARAMS[setting]
human_config_data = common.load_human_config_json()
# params["data_dir"] = os.path.dirname(ROOT_DIR) + "/OpenLockResults/subjects"
params["data_dir"] = human_config_data["HUMAN_SAVE_DIR"]
params["src_dir"] = "/tmp/openlocklearner/" + str(hash(
time.time())) + "/src/"
params["use_physics"] = True
params["effect_probabilities"] = generate_effect_probabilities()
# this section randomly selects a testing and training scenario
# train_scenario_name, test_scenario_name = select_random_scenarios()
# params['train_scenario_name'] = train_scenario_name
# params['test_scenario_name'] = test_scenario_name
scenario = select_scenario(params["train_scenario_name"])
def load_human_data_pickle():
human_config_data = load_human_config_json()
with open(human_config_data["HUMAN_PICKLE_DATA_PATH"], "rb") as infile:
human_subjects = pickle.load(infile)
return human_subjects
def load_human_data_json():
human_config_data = load_human_config_json()
return load_subjects_from_dir(human_config_data["HUMAN_JSON_DATA_PATH"])