old_contents: import orca
  from activitysim import abm
  from activitysim.core import tracing
  import pandas as pd
  import numpy as np
  import os
  
  from activitysim.core.tracing import print_elapsed_time
  
  from activitysim.core import pipeline
  import extensions
  
  
  # comment out the line below to default base seed to 0 random seed
  # so that run results are reproducible
  # pipeline.set_rn_generator_base_seed(seed=None)
  
  
  tracing.config_logger()
  
  t0 = print_elapsed_time()
  
  _MODELS = [
      'compute_accessibility',
      'school_location_simulate',
      'workplace_location_sample',
      'workplace_location_logsums',
      'workplace_location_simulate',
      'auto_ownership_simulate',
      'cdap_simulate',
      'mandatory_tour_frequency',
      'mandatory_scheduling',
      'non_mandatory_tour_frequency',
      'destination_choice',
      'non_mandatory_scheduling',
      'tour_mode_choice_simulate',
      'create_simple_trips',
      'trip_mode_choice_simulate'
  ]
  
  
  # If you provide a resume_after argument to pipeline.run
  # the pipeline manager will attempt to load checkpointed tables from the checkpoint store
  # and resume pipeline processing on the next submodel step after the specified checkpoint
  resume_after = None
  # resume_after = 'mandatory_scheduling'
  
  pipeline.run(models=_MODELS, resume_after=resume_after)
  
  print "\n#### run completed"
  
  # retrieve the state of a checkpointed table after a specific model was run
  df = pipeline.get_table(table_name="persons", checkpoint_name="school_location_simulate")
  print "\npersons table columns after school_location_simulate:", df.columns.values
  
  # get_table without checkpoint_name returns the latest version of the table
  df = pipeline.get_table("tours")
  print "\ntour_type value counts\n", df.tour_type.value_counts()
  
  # get_table for a computed (non-checkpointed, internal, orca) table
  # return the most recent value of a (non-checkpointed, internal) computed table
  df = pipeline.get_table("persons_merged")
  df = df[['household_id', 'age', 'auPkTotal', 'roundtrip_auto_time_to_work']]
  print "\npersons_merged selected columns\n", df.head(20)
  
  # write final versions of all checkpointed dataframes to CSV files to review results
  for table_name in pipeline.checkpointed_tables():
      file_name = "final_%s_table.csv" % table_name
      file_path = os.path.join(orca.get_injectable("output_dir"), file_name)
      pipeline.get_table(table_name).to_csv(file_path)
  
  # tables will no longer be available after pipeline is closed
  pipeline.close()
  
  # write checkpoints (this can be called whether of not pipeline is open)
  file_path = os.path.join(orca.get_injectable("output_dir"), "checkpoints.csv")
  pipeline.get_checkpoints().to_csv(file_path)
  
  t0 = print_elapsed_time("all models", t0)
  
new_contents: import orca
  from activitysim import abm
  from activitysim.core import tracing
  import pandas as pd
  import numpy as np
  import os
  
  from activitysim.core.tracing import print_elapsed_time
  
  from activitysim.core import pipeline
  import extensions
  
  
  # comment out the line below to default base seed to 0 random seed
  # so that run results are reproducible
  # pipeline.set_rn_generator_base_seed(seed=None)
  
  
  tracing.config_logger()
  
  t0 = print_elapsed_time()
  
  _MODELS = [
      'compute_accessibility',
      'school_location_sample',
      'school_location_logsums',
      'school_location_simulate',
      'workplace_location_sample',
      'workplace_location_logsums',
      'workplace_location_simulate',
      'auto_ownership_simulate',
      'cdap_simulate',
      'mandatory_tour_frequency',
      'mandatory_scheduling',
      'non_mandatory_tour_frequency',
      'destination_choice',
      'non_mandatory_scheduling',
      'tour_mode_choice_simulate',
      'create_simple_trips',
      'trip_mode_choice_simulate'
  ]
  
  
  # If you provide a resume_after argument to pipeline.run
  # the pipeline manager will attempt to load checkpointed tables from the checkpoint store
  # and resume pipeline processing on the next submodel step after the specified checkpoint
  resume_after = None
  # resume_after = 'mandatory_scheduling'
  
  pipeline.run(models=_MODELS, resume_after=resume_after)
  
  print "\n#### run completed"
  
  # retrieve the state of a checkpointed table after a specific model was run
  df = pipeline.get_table(table_name="persons", checkpoint_name="school_location_simulate")
  print "\npersons table columns after school_location_simulate:", df.columns.values
  
  # get_table without checkpoint_name returns the latest version of the table
  df = pipeline.get_table("tours")
  print "\ntour_type value counts\n", df.tour_type.value_counts()
  
  # get_table for a computed (non-checkpointed, internal, orca) table
  # return the most recent value of a (non-checkpointed, internal) computed table
  df = pipeline.get_table("persons_merged")
  df = df[['household_id', 'age', 'auPkTotal', 'roundtrip_auto_time_to_work']]
  print "\npersons_merged selected columns\n", df.head(20)
  
  # write final versions of all checkpointed dataframes to CSV files to review results
  for table_name in pipeline.checkpointed_tables():
      file_name = "final_%s_table.csv" % table_name
      file_path = os.path.join(orca.get_injectable("output_dir"), file_name)
      pipeline.get_table(table_name).to_csv(file_path)
  
  # tables will no longer be available after pipeline is closed
  pipeline.close()
  
  # write checkpoints (this can be called whether of not pipeline is open)
  file_path = os.path.join(orca.get_injectable("output_dir"), "checkpoints.csv")
  pipeline.get_checkpoints().to_csv(file_path)
  
  t0 = print_elapsed_time("all models", t0)
  
subject: update example for school_location logsums