new_contents: import os
  import sys
  import copy
  from fermipy.gtanalysis import GTAnalysis
  from haloanalysis.fit_funcs import fit_halo
  
  import numpy as np
  import itertools
  import argparse
  import yaml
  
  if __name__ == '__main__':
  
      usage = "usage: %(prog)s [config file]"
      description = "Run fermipy analysis chain."
      parser = argparse.ArgumentParser(usage=usage,description=description)
  
      parser.add_argument('--config', default = 'sample_config.yaml')
      parser.add_argument('--source', default = None)
  
      args = parser.parse_args()
  
      config = yaml.load(open(args.config,'r'))
  
      halo_width = np.logspace(-1.5,0,4)
      halo_index = np.array([1.5,2.0,2.5])
  
      
  #    halo_source_dict = {
  #        'SpectrumType' : 'PowerLaw', 
  #        'Index' : 2.0, 
  #        'Scale' : 1000,
  #        'Prefactor' : { 'value' : 1E-5, 'scale' : 1e-13 },
  #        'SpatialModel' : 'GaussianSource',
  #        'SpatialWidth' : 1.0
  #        }
  
      gta = GTAnalysis(args.config,logging={'verbosity' : 3})
  
      gta.setup()
      gta.simulate_roi(restore=True)
  
      ext_fit_data = []
      halo_fit_data = []
  
      gta.write_roi('base_model',save_model_map=False,make_plots=False)
  
      for i in range(1):
  
          gta.load_xml('base_model')
  
          gta.simulate_roi(randomize=False)
  
          gta.free_source('testsource')
          gta.update_source('testsource')
  #        gta.fit()
          gta.free_sources(free=False)
  
          gta.extension('testsource',width=np.logspace(-2.5,-0.5,9))
  
          ext_fit_data += [copy.deepcopy(gta.roi['testsource'])]
  
          gta.write_roi('fit%04i'%i,save_model_map=False,make_plots=False,
                        format='npy')
  
          fit_halo(gta,'fit%04i'%i,'testsource',halo_width,halo_index)
          
      np.save(os.path.join(gta._savedir,'ext_fit_data.npy'),ext_fit_data)
  
  
subject: Add script for asimov MC analysis.