text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  
  ######################################################################
  # Plot 1 figure with the original TS, the encoded TS and the decoded #
  # TS                                                                 #
  ######################################################################
  
  import context
  import numpy as np
  import matplotlib.pyplot as plt
  
  from src.models import autoencoder
  from src.networks.autoencoder import AutoEncoder
  
  
  if __name__ == '__main__':
      # load an already trained auto-encoder model
      ae1 = AutoEncoder(load_models='encoder-Model1_test15', version=0)
  
  
      # predict encoder
      ts_origi = np.load('../../dataset_new/train_TS/TS_00003.npy') #[200:] # adapt you path
      width, height = ts_origi.shape
      ts_origi = np.array([ts_origi], dtype='float32')
      ts_enc = ae1.predict(ts_origi.reshape(len(ts_origi), width, height, 1), batch_size=1)
      print(ts_origi.dtype)
      print(ts_enc.dtype)
      tsori = ts_origi[0,:,:]
      tsenc = np.array(ts_enc[0,:,:])
      plt.subplot(1, 3, 1)
      plt.imshow(tsori)
      plt.title('Original Timestack')
      plt.subplot(1, 3, 2)
      plt.imshow(tsenc)
      plt.title('Encoded Timestack')
      plt.subplot(1, 3, 3)
      plt.imshow(tsori)
      plt.title('Decoded Timestack')
      plt.show()
  