if cfg.five_players:
csv = Tools.cut_csv_to_pitchers(csv)
print("Only the five players with most data are included")
# GET DATA
data, labels = Tools.get_data_from_csv(csv,
label_name,
min_length=cfg.nr_frames)
print("Data shape:", data.shape)
# data = np.load("data_test.npy")
# labels = np.load("labels_test.npy")
# 4. Change labels to super classes (only for the pitch type!)
if cfg.super_classes:
labels = Tools.labels_to_classes(labels)
print("Labels are transformed to superclasses")
## POSSIBLE TO SHIFT AND FLIP DATA TO TRAIN ON MORE GENERAL DATA
# data_old, _ = Tools.shift_data(data, labels, shift_labels = False, max_shift=30)
# data_new = Tools.flip_x_data(data_old.copy()) #[:len(data_old)//2]
# data = np.append(data_new, data_old, axis=0)
# labels = np.append(labels, labels, axis=0)
# print(data.shape, labels.shape)
data = Tools.normalize(data)
if args.training:
training(data, labels, args.save_path)
else:
testing(data, labels, args.save_path)
