synapses = networks[1][5]
network_name = networks[1][6]
#Save Config
dict_config = {
"neurons": neurons,
"sensory_neurons": (sensory_neurons_in, sensory_neurons_out),
"save": to_save,
"step_input": stimuli_dict,
"synapses": synapses,
"name": network_name
}
config_dict_hash = hashDict(dict_config)
saveKey(config_dict_hash + "_input"
, dict_config
, output_dir)
saveFile(
config_file_name
, output_dir + "/" + config_dict_hash + "_input.py")
#Define robot
if sensory_neurons_in or sensory_neurons_out:
robot = RobotYARP(mode=args.robot_mode)
else:
robot = None
#TODO: Reset robot original position? (and maybe gz-world status?)
if robot:
if args.reset_position or args.reset_only:
print "RESETTING"
#fix parentheses:
commands = [ i for i in commands if not missing(i) ]
#set just to be sure no duplicate runs
real_commands = set(commands)
#TODO:
#FIXME: remove "--show-image" etc to prevent different hashes of same config
session_hash = hashDict(real_commands)
#Save commands list
is_folder("./commands")
commands_file = "./commands/" + session_hash + "_commands"
cprint ("Saving %s commands to file: %s" % (len(real_commands), commands_file), 'info')
saveKey(commands_file, commands)
# Debug: re-enable in release
# cprint("Running in 5s", "warning")
# time.sleep(5)
#Start
is_folder (output_dir)
cprint("We'll use %s as output dir" % (output_dir), 'info')
#Let's run the simulations (surely not the best way, but does the job)
#TODO: add the loop inside Runner?
start = time.time()
try:
run = import_history(output_dir + "/" + session_hash + "_batch")
recover_from_lap = run["cycle"]
