n_iter = 500000
n_gen = 50
TPREFERRED = 26
# Indicates the baseline bout frequency
TRAIN_BOUT_FREQ = 1
def mpath(path):
return base_path + path[:-1] # need to remove trailing slash
if __name__ == "__main__":
# load training data for scaling - dependent on bout frequency
if TRAIN_BOUT_FREQ == 1:
std = GradientData.load_standards("gd_training_data.hdf5")
elif TRAIN_BOUT_FREQ == 0.5:
std = GradientData.load_standards("gd_05Hz_training_data.hdf5")
elif TRAIN_BOUT_FREQ == 2:
std = GradientData.load_standards("gd_2Hz_training_data.hdf5")
else:
raise Exception(
"No training data has been generated for the requested bout frequency"
)
# evolve each 512 network unless it has been done before
for p in paths_512:
model_path = mpath(p)
savedir = model_path + '/evolve/'
if os.path.exists(savedir):
print(
mo_type = ""
while mo_type != "c" and mo_type != "z":
mo_type = input("Please select either (z)ebrafish or (c) elegans simulation [z/c]:")
mo_type = mo_type.lower()
n_steps = 2000000
TPREFERRED = 25
root = tk.Tk()
root.update()
root.withdraw()
print("Select model directory")
model_dir = filedialog.askdirectory(title="Select directory with model checkpoints", initialdir="./model_data/")
root.update()
mdata = ModelData(model_dir)
# load training data for scaling
if mo_type == "z":
std = GradientData.load_standards("gd_training_data.hdf5")
else:
std = GradientData.load_standards("ce_gd_training_data.hdf5")
sim_type = ""
while sim_type != "l" and sim_type != "r":
sim_type = input("Please select either (l)inear or (r)adial simulation [l/r]:")
if mo_type == "z":
mot = MoTypes(False)
else:
mot = MoTypes(True)
gpn_naive = mot.network_model()
gpn_naive.load(mdata.ModelDefinition, mdata.FirstCheckpoint)
gpn_trained = mot.network_model()
gpn_trained.load(mdata.ModelDefinition, mdata.LastCheckpoint)
if sim_type == "l":
sim_type = "x" # so we call run_simulation correctly later