def process_signal(self):
self.counter += 1
self.output_buffer = np.zeros([self.input_buffer.shape[0], self.feature_vector_size])
threads = []
thread_list = [i for i in range(0, self.number_of_threads)]
for thread_id in thread_list:
thread = PreProcessor(thread_id, self.input_buffer, self.output_buffer, config=self.config)
thread.start()
threads.append(thread)
for t in threads:
t.join()
# with open(self.train_dir + "/feature_vectors.csv", 'a') as f:
# np.savetxt(f, self.output_buffer, delimiter=',', fmt='%.18e')
clip_label = get_label(1, self.number_of_class)
clip_filename = draw_sample_plot_and_save(self.output_buffer.flatten(), "/channel", self.thread_id, self.config)
sample = create_sample_from_image(clip_filename, clip_label, self.config)
# sample = create_sample_from_data(self.output_buffer.flatten(), class_label)
self.writer.write(sample.SerializeToString())
self.send_noise_data(json.dumps(self.input_buffer.tolist()))
self.send_preprocessed_data(json.dumps(self.output_buffer.tolist()))
