How to use the activitysim.core.simulate.read_model_spec function in activitysim

def run_location_simulate(
        segment_name,
        persons_merged,
        location_sample_df,
        skim_dict,
        dest_size_terms,
        model_settings,
        chunk_size, trace_label):
    """
    run location model on location_sample annotated with mode_choice logsum
    to select a dest zone from sample alternatives
    """
    assert not persons_merged.empty

    model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])

    # FIXME - MEMORY HACK - only include columns actually used in spec
    chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
    choosers = persons_merged[chooser_columns]

    alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]

    # alternatives are pre-sampled and annotated with logsums and pick_count
    # but we have to merge additional alt columns into alt sample list
    alternatives = \
        pd.merge(location_sample_df, dest_size_terms,
                 left_on=alt_dest_col_name, right_index=True, how="left")

    logger.info("Running %s with %d persons" % (trace_label, len(choosers)))

    # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers

def run_destination_sample(
        spec_segment_name,
        tours,
        persons_merged,
        model_settings,
        skim_dict,
        destination_size_terms,
        chunk_size, trace_label):

    model_spec_file_name = model_settings['SAMPLE_SPEC']
    model_spec = simulate.read_model_spec(file_name=model_spec_file_name)
    model_spec = model_spec[[spec_segment_name]]

    # merge persons into tours
    choosers = pd.merge(tours, persons_merged, left_on='person_id', right_index=True, how='left')
    # FIXME - MEMORY HACK - only include columns actually used in spec
    chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
    choosers = choosers[chooser_columns]

    constants = config.get_model_constants(model_settings)

    sample_size = model_settings["SAMPLE_SIZE"]
    alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]

    logger.info("running %s with %d tours", trace_label, len(choosers))

    # create wrapper with keys for this lookup - in this case there is a workplace_taz

def tour_mode_choice_spec(model_settings):

    assert 'SPEC' in model_settings

    return simulate.read_model_spec(file_name=model_settings['SPEC'])

def run_destination_simulate(
        spec_segment_name,
        tours,
        persons_merged,
        destination_sample,
        model_settings,
        skim_dict,
        destination_size_terms,
        chunk_size, trace_label):
    """
    run destination_simulate on tour_destination_sample
    annotated with mode_choice logsum to select a destination from sample alternatives
    """

    model_spec_file_name = model_settings['SPEC']
    model_spec = simulate.read_model_spec(file_name=model_spec_file_name)
    model_spec = model_spec[[spec_segment_name]]

    # merge persons into tours
    choosers = pd.merge(tours,
                        persons_merged,
                        left_on='person_id', right_index=True, how='left')
    # FIXME - MEMORY HACK - only include columns actually used in spec
    chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
    choosers = choosers[chooser_columns]

    alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]
    origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME']

    # alternatives are pre-sampled and annotated with logsums and pick_count
    # but we have to merge size_terms column into alt sample list
    destination_sample['size_term'] = \

and then select a sample subset of potential locations

    The sample subset is generated by making multiple choices ( number of choices)
    which results in sample containing up to  choices for each choose (e.g. person)
    and a pick_count indicating how many times that choice was selected for that chooser.)

    person_id,  dest_TAZ, rand,            pick_count
    23750,      14,       0.565502716034,  4
    23750,      16,       0.711135838871,  6
    ...
    23751,      12,       0.408038878552,  1
    23751,      14,       0.972732479292,  2
    """
    assert not persons_merged.empty

    model_spec = simulate.read_model_spec(file_name=model_settings['SAMPLE_SPEC'])

    # FIXME - MEMORY HACK - only include columns actually used in spec
    chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
    choosers = persons_merged[chooser_columns]

    alternatives = dest_size_terms

    sample_size = model_settings["SAMPLE_SIZE"]
    alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]

    logger.info("Running %s with %d persons" % (trace_label, len(choosers.index)))

    # create wrapper with keys for this lookup - in this case there is a TAZ in the choosers
    # and a TAZ in the alternatives which get merged during interaction
    # (logit.interaction_dataset suffixes duplicate chooser column with '_chooser')
    # the skims will be available under the name "skims" for any @ expressions

def cdap_indiv_spec():
    """
    spec to compute the activity utilities for each individual hh member
    with no interactions with other household members taken into account
    """
    return simulate.read_model_spec(file_name='cdap_indiv_and_hhsize1.csv')

def atwork_subtour_scheduling(
        tours,
        persons_merged,
        tdd_alts,
        skim_dict,
        chunk_size,
        trace_hh_id):
    """
    This model predicts the departure time and duration of each activity for at work subtours tours
    """

    trace_label = 'atwork_subtour_scheduling'
    model_settings = config.read_model_settings('tour_scheduling_atwork.yaml')
    model_spec = simulate.read_model_spec(file_name='tour_scheduling_atwork.csv')

    persons_merged = persons_merged.to_frame()

    tours = tours.to_frame()
    subtours = tours[tours.tour_category == 'atwork']

    # - if no atwork subtours
    if subtours.shape[0] == 0:
        tracing.no_results(trace_label)
        return

    logger.info("Running %s with %d tours", trace_label, len(subtours))

    # preprocessor
    constants = config.get_model_constants(model_settings)
    od_skim_wrapper = skim_dict.wrap('origin', 'destination')

def joint_tour_scheduling(
        tours,
        persons_merged,
        tdd_alts,
        chunk_size,
        trace_hh_id):
    """
    This model predicts the departure time and duration of each joint tour
    """
    trace_label = 'joint_tour_scheduling'
    model_settings = config.read_model_settings('joint_tour_scheduling.yaml')
    model_spec = simulate.read_model_spec(file_name='tour_scheduling_joint.csv')

    tours = tours.to_frame()
    joint_tours = tours[tours.tour_category == 'joint']

    # - if no joint tours
    if joint_tours.shape[0] == 0:
        tracing.no_results(trace_label)
        return

    # use inject.get_table as this won't exist if there are no joint_tours
    joint_tour_participants = inject.get_table('joint_tour_participants').to_frame()

    persons_merged = persons_merged.to_frame()

    logger.info("Running %s with %d joint tours", trace_label, joint_tours.shape[0])