def show_stack_info(model):
p = Plotter(ax_num=4)
lb = LabelBinarizer()
#load spectrum
spec = np.load(args['stack'])[args['index']]
#classify spectrum
p1, p2, p_stack = classify(model, spec, lb)
#load true stack parameters
name = args['stack'].split("/")[-1][:-4]
batch_dir_list = args['stack'].split("/")[:-2]
batch_dir = ""
for f in batch_dir_list:
batch_dir += f
batch_dir += "/"
with open(f"{batch_dir}params/{name}.pickle", "rb") as f:
stack_params = pickle.load(f)
t1, t2, t_stack = stack_params[args['index']]
pred_text = p.write_text(p1, p2, p_stack, loss_val=0)
true_text = p.write_text(t1, t2, t_stack, loss_val=0)
p.double_spec(spec, pred_text, true_text)
plt.show()
def NN_test_loop(crawler, lb):
while True:
while True:
spectrum, true1, true2, true_stack = create_random_stack(
crawler, param_dict)
if np.max(spectrum) > 0.1:
break
l1, l2, stack = classify(model, spectrum, lb)
plotter = Plotter(ax3_on=True)
pred_text = plotter.write_text(l1, l2, stack, loss_val=0)
true_text = plotter.write_text(true1, true2, true_stack, loss_val=0)
plotter.double_text(spectrum, pred_text, true_text)
plt.show()