make mask colours easier to see
" ct_uid = ct_uid.numpy().decode()\n",
" \n",
" orbits = tf.math.reduce_max(output_array[:,:,-2:], axis=-1)\n",
- " output_with_lens_reduced = tf.concat([output_array[:,:,:-2], orbits[:,:,None]], axis=-1)\n",
+ " output_three_channel = tf.concat([\n",
+ " orbits[:,:,None], \n",
+ " output_array[:,:,1:2], \n",
+ " output_array[:,:,0:1]\n",
+ " ], axis=-1)\n",
" \n",
" display.display(display.Markdown(f\"## {ct_uid}\"))\n",
- " diagnostic_plotting(x_grid, y_grid, input_array, output_with_lens_reduced)\n",
+ " diagnostic_plotting(x_grid, y_grid, input_array, output_three_channel)\n",
" plt.show() \n",
" \n",
" scaled_input_array = tf.convert_to_tensor(transform.downscale_local_mean(input_array, (dimension_downscale, dimension_downscale, 1)))\n",
" scaled_output_array = tf.convert_to_tensor(transform.downscale_local_mean(\n",
- " output_with_lens_reduced, (dimension_downscale, dimension_downscale, 1)))\n",
+ " output_three_channel, (dimension_downscale, dimension_downscale, 1)))\n",
" \n",
" new_x_grid = tf.convert_to_tensor(x_grid[dimension_downscale//2::dimension_downscale])\n",
" new_y_grid = tf.convert_to_tensor(y_grid[dimension_downscale//2::dimension_downscale])\n",
