old_contents: import os
  import json
  import pickle
  import itertools
  from indra.util import _require_python3
  import indra.tools.assemble_corpus as ac
  from indra.tools.reading.submit_reading_pipeline_aws import \
      submit_run_reach, submit_combine, wait_for_complete
  import assemble_pysb
  import process_data
  from util import *
  
  
  def run_reading(pmid_fname):
      """Run reading on Amazon to produce a pickle of INDRA Statements
  
      This needs to be done only once, after that the resulting pickle file
      can be loaded.
      """
      # Submit reading
      job_list = submit_run_reach(basen, pmid_fname)
      # Wait for reading to complete
      reading_res = wait_for_complete(job_list)
      # Submid pickle combine job
      combine_res = submit_combine(basen, job_list)
      # Download the file and save
  
  
  if __name__ == '__main__':
      # Load the data and get the gene names
      data = process_data.read_rppa_data()
      gene_names = process_data.get_gene_names(data)
  
      # If generic assembly needs to be done (instead of just loading the result)
      # set this to True
      reassemble = False
  
      # The file in which the preassembled statements will be saved
      pre_stmts_file = prefixed_pkl('preassembled')
      if reassemble:
          # Load various files that were previously produced
          sources = ['reach', 'trips', 'bel', 'biopax', 'phosphosite', 'r3',
                     'sparser_1_fixed']
          stmts = []
          for source in sources:
              stmts += ac.load_statements(prefixed_pkl(source))
          stmts = ac.filter_no_hypothesis(stmts)
          # Fix grounding and filter to grounded entities and for proteins,
          # filter to the human ones
          stmts = ac.map_grounding(stmts)
          stmts = ac.filter_grounded_only(stmts)
          stmts = ac.filter_human_only(stmts)
          # Combinatorially expand protein families
          stmts = ac.expand_families(stmts)
          # Apply a strict filter to statements based on the gene names
          stmts = ac.filter_gene_list(stmts, gene_names, 'all')
          # Fix errors in references to protein sequences
          stmts = ac.map_sequence(stmts)
          # Run preassembly and save result
          stmts = ac.run_preassembly(stmts, return_toplevel=False)
          ac.dump_statements(stmts, pre_stmts_file)
  
      # Load the preassembled statements
      stmts = ac.load_statements(pre_stmts_file)
      # Run assembly into a PySB model
      assemble_pysb.assemble_pysb(stmts, gene_names)
  
new_contents: import os
  import json
  import pickle
  import itertools
  from indra.util import _require_python3
  import indra.tools.assemble_corpus as ac
  from indra.tools.reading.submit_reading_pipeline_aws import \
      submit_run_reach, submit_combine, wait_for_complete
  import assemble_pysb
  import process_data
  from util import *
  
  
  def run_reading(pmid_fname):
      """Run reading on Amazon to produce a pickle of INDRA Statements
  
      This needs to be done only once, after that the resulting pickle file
      can be loaded.
      """
      # Submit reading
      job_list = submit_run_reach(basen, pmid_fname)
      # Wait for reading to complete
      reading_res = wait_for_complete(job_list)
      # Submid pickle combine job
      combine_res = submit_combine(basen, job_list)
      # Download the file and save
  
  
  if __name__ == '__main__':
      # Load the data and get the gene names
      data = process_data.read_rppa_data()
      gene_names = process_data.get_gene_names(data)
  
      # If generic assembly needs to be done (instead of just loading the result)
      # set this to True
      reassemble = False
  
      # The file in which the preassembled statements will be saved
      pre_stmts_file = prefixed_pkl('preassembled')
      if reassemble:
          # Load various files that were previously produced
          sources = ['reach', 'trips', 'bel', 'biopax', 'phosphosite', 'r3',
                     'sparser']
          stmts = []
          for source in sources:
              stmts += ac.load_statements(prefixed_pkl(source))
          stmts = ac.filter_no_hypothesis(stmts)
          # Fix grounding and filter to grounded entities and for proteins,
          # filter to the human ones
          stmts = ac.map_grounding(stmts)
          stmts = ac.filter_grounded_only(stmts)
          stmts = ac.filter_human_only(stmts)
          # Combinatorially expand protein families
          stmts = ac.expand_families(stmts)
          # Apply a strict filter to statements based on the gene names
          stmts = ac.filter_gene_list(stmts, gene_names, 'all')
          # Fix errors in references to protein sequences
          stmts = ac.map_sequence(stmts)
          # Run preassembly and save result
          stmts = ac.run_preassembly(stmts, return_toplevel=False)
          ac.dump_statements(stmts, pre_stmts_file)
  
      # Load the preassembled statements
      stmts = ac.load_statements(pre_stmts_file)
      # Run assembly into a PySB model
      assemble_pysb.assemble_pysb(stmts, gene_names, contextualize=True)
  
subject: Add final Sparser output to assembly and contextualize