import numpy as np import torch import ST J = 8 L = 4 M = 512 N = 512 filter_set = ST.FiltersSet(M, N, J, L) # generate and save morlet filter bank. "single" means single precision save_dir = '#####' filter_set.generate_morlet(if_save=True, save_dir=save_dir, precision='single') # load filter bank filters_set = np.load( save_dir + 'filters_set_M' + str(M) + 'N' + str(N) + 'J' + str(J) + 'L' + str(L) + '_single.npy', allow_pickle=True )[0]['filters_set'] # define ST calculator ST_calculator = ST.ST_2D(filters_set, J, L, device='gpu') ############ DEFINE DATA ARRAY ######### data = np.empty((30, M, N), dtype=np.float32) ################## ST ################## # input data should be a numpy array of images with dimensions (N_image, M, N) # output are torch tensors with assigned computing device, e.g., cuda() or cpu