text: from __future__ import print_function, division
  import SimpleITK as sitk
  import numpy as np
  import csv
  from glob import glob
  import pandas as pd
  import os
  try:
      from tqdm import tqdm # long waits are not fun
  except:
      print('TQDM does make much nicer wait bars...')
      tqdm = lambda x: x
  
  #Some helper functions
  
  def make_mask(center,diam,z,width,height,spacing,origin):
      '''
  Center : centers of circles px -- list of coordinates x,y,z
  diam : diameters of circles px -- diameter
  widthXheight : pixel dim of image
  spacing = mm/px conversion rate np array x,y,z
  origin = x,y,z mm np.array
  z = z position of slice in world coordinates mm
      '''
      mask = np.zeros([height,width]) # 0's everywhere except nodule swapping x,y to match img
      #convert to nodule space from world coordinates
  
      # Defining the voxel range in which the nodule falls
      v_center = (center-origin)/spacing
      v_diam = int(diam/spacing[0]+5)
      v_xmin = np.max([0,int(v_center[0]-v_diam)-5])
      v_xmax = np.min([width-1,int(v_center[0]+v_diam)+5])
      v_ymin = np.max([0,int(v_center[1]-v_diam)-5]) 
      v_ymax = np.min([height-1,int(v_center[1]+v_diam)+5])
  
      v_xrange = range(v_xmin,v_xmax+1)
      v_yrange = range(v_ymin,v_ymax+1)
  
      # Convert back to world coordinates for distance calculation
      x_data = [x*spacing[0]+origin[0] for x in range(width)]
      y_data = [x*spacing[1]+origin[1] for x in range(height)]
  
      # Fill in 1 within sphere around nodule
      for v_x in v_xrange:
          for v_y in v_yrange:
              p_x = spacing[0]*v_x + origin[0]
              p_y = spacing[1]*v_y + origin[1]
              if np.linalg.norm(center-np.array([p_x,p_y,z]))<=diam:
                  mask[int((p_y-origin[1])/spacing[1]),int((p_x-origin[0])/spacing[0])] = 1.0
      return(mask)
  
  def matrix2int16(matrix):
      ''' 
  matrix must be a numpy array NXN
  Returns uint16 version
      '''
      m_min= np.min(matrix)
      m_max= np.max(matrix)
      matrix = matrix-m_min
      return(np.array(np.rint( (matrix-m_min)/float(m_max-m_min) * 65535.0),dtype=np.uint16))
  
  ############
  #
  
  
  
  #####################
  #
  # Helper function to get rows in data frame associated 
  # with each file
  def get_filename(file_list, case):
      for f in file_list:
          if case in f:
              return(f)
  #
  
  
  
  if __name__ == '__main__':
      # Getting list of image files
      
      luna_path = "/home/cse/dual/cs5130287/Luna2016/"
      luna_scratch_path = "/scratch/cse/dual/cs5130287/Luna2016/"
      luna_subset_path = luna_scratch_path+"subset0/"
      output_path = "/scratch/cse/dual/cs5130287/Luna2016/output_final/"
      file_list=glob(luna_subset_path+"*.mhd")
      # The locations of the nodes
      df_node = pd.read_csv(luna_path+"annotations.csv")
      df_node["file"] = df_node["seriesuid"].map(lambda file_name: get_filename(file_list, file_name))
      df_node = df_node.dropna()
  
      #####
      #
      # Looping over the image files
      #
      for fcount, img_file in enumerate(tqdm(file_list)):
          mini_df = df_node[df_node["file"]==img_file] #get all nodules associate with file
          if mini_df.shape[0]>0: # some files may not have a nodule--skipping those 
              # load the data once
              itk_img = sitk.ReadImage(img_file) 
              img_array = sitk.GetArrayFromImage(itk_img) # indexes are z,y,x (notice the ordering)
              num_z, height, width = img_array.shape        #heightXwidth constitute the transverse plane
              origin = np.array(itk_img.GetOrigin())      # x,y,z  Origin in world coordinates (mm)
              spacing = np.array(itk_img.GetSpacing())    # spacing of voxels in world coor. (mm)
              # go through all nodes (why just the biggest?)
              for node_idx, cur_row in mini_df.iterrows():       
                  node_x = cur_row["coordX"]
                  node_y = cur_row["coordY"]
                  node_z = cur_row["coordZ"]
                  diam = cur_row["diameter_mm"]
                  # just keep 3 slices
                  imgs = np.ndarray([3,height,width],dtype=np.float32)
                  masks = np.ndarray([3,height,width],dtype=np.uint8)
                  center = np.array([node_x, node_y, node_z])   # nodule center
                  v_center = np.rint((center-origin)/spacing)  # nodule center in voxel space (still x,y,z ordering)
                  for i, i_z in enumerate(np.arange(int(v_center[2])-1,
                                   int(v_center[2])+2).clip(0, num_z-1)): # clip prevents going out of bounds in Z
                      mask = make_mask(center, diam, i_z*spacing[2]+origin[2],
                                       width, height, spacing, origin)
                      masks[i] = mask
                      imgs[i] = img_array[i_z]
                  np.save(os.path.join(output_path,"images_%04d_%04d.npy" % (fcount, node_idx)),imgs)
                  np.save(os.path.join(output_path,"masks_%04d_%04d.npy" % (fcount, node_idx)),masks)
  