#Functions for parsing command line arguments for ome ilastik prep
import argparse


def ParseInputDataExtract():
   """Function for parsing command line arguments for input to single-cell
   data extraction"""

#if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument('--masks',nargs='+', required=True)
   parser.add_argument('--image', required=True)
   parser.add_argument('--channel_names', required=True)
   parser.add_argument('--output', required=True)
   parser.add_argument(
      '--mask_props', nargs = "+",
      help="""
         Space separated list of additional metrics to be calculated for every mask.
         This is for metrics that depend only on the cell mask. If the metric depends
         on signal intensity, use --intensity-props instead.
         See list at https://scikit-image.org/docs/dev/api/skimage.measure.html#regionprops
      """
   )
   parser.add_argument(
      '--intensity_props', nargs = "+",
      help="""
         Space separated list of additional metrics to be calculated for every marker separately.
         By default only mean intensity is calculated.
         If the metric doesn't depend on signal intensity, use --mask-props instead.
         See list at https://scikit-image.org/docs/dev/api/skimage.measure.html#regionprops
         Additionally available is gini_index, which calculates a single number
         between 0 and 1, representing how unequal the signal is distributed in each region.
         See https://en.wikipedia.org/wiki/Gini_coefficient
      """
   )
   #parser.add_argument('--suffix')
   args = parser.parse_args()
   #Create a dictionary object to pass to the next function
   dict = {'masks': args.masks, 'image': args.image,\
    'channel_names': args.channel_names,'output':args.output,
    'intensity_props': set(args.intensity_props if args.intensity_props is not None else []).union(["intensity_mean"]),
    'mask_props': args.mask_props,
   }
   #Print the dictionary object
   print(dict)
   #Return the dictionary
   return dict