How to use the workflows.display_experiments.create_workflow_list_technicians_walkarounds function in workflows

from hyperstream.utils import StreamNotFoundError, reconstruct_interval

    hyperstream = HyperStream(loglevel=loglevel)
    M = hyperstream.channel_manager.memory
    D = hyperstream.channel_manager.mongo
    A = hyperstream.channel_manager.assets

    workflow_id0 = "list_technicians_walkarounds"

    if delete_existing_workflows:
        hyperstream.workflow_manager.delete_workflow(workflow_id0)

    try:
        w0 = hyperstream.workflow_manager.workflows[workflow_id0]
    except KeyError:
        w0 = create_workflow_list_technicians_walkarounds(hyperstream, house=house, safe=False)
        hyperstream.workflow_manager.commit_workflow(workflow_id0)
    time_interval = TimeInterval.up_to_now()
    w0.execute(time_interval)

    # from datetime import timedelta
    # time_interval.end += timedelta(milliseconds=1)
    df = M[StreamId('experiments_dataframe', dict(house=house))].window().values()[0]
    experiment_ids = set([df['experiment_id'][i - 1] for i in selection])

    experiment_ids_str = '_'.join(experiment_ids)

    create_selected_localisation_plates(hyperstream)

    # Ensure the model is overwritten if it's already there
    model_id = StreamId(
        name="location_prediction",

description="All houses",
        meta_data_id="house",
        values=[],
        complement=True,
        parent_plate=None
    )

    workflow_id = "list_technicians_walkarounds"

    if delete_existing_workflows:
        hyperstream.workflow_manager.delete_workflow(workflow_id)

    try:
        w = hyperstream.workflow_manager.workflows[workflow_id]
    except KeyError:
        w = create_workflow_list_technicians_walkarounds(hyperstream, house, safe=False)
        hyperstream.workflow_manager.commit_workflow(workflow_id)
    time_interval = TimeInterval.up_to_now()
    w.execute(time_interval)
    
    print('number of sphere non_empty_streams: {}'.format(len(S.non_empty_streams)))
    print('number of memory non_empty_streams: {}'.format(len(M.non_empty_streams)))
    
    df = M[StreamId('experiments_dataframe', dict(house=house))].window().values()[0]
    # arrow.get(x).humanize()
    # df['start'] = df['start'].map('{:%Y-%m-%d %H:%M:%S}'.format)
    df['duration'] = df['end'] - df['start']
    df['start'] = map(lambda x: '{:%Y-%m-%d %H:%M:%S}'.format(x), df['start'])
    df['end'] = map(lambda x: '{:%Y-%m-%d %H:%M:%S}'.format(x), df['end'])
    # df['duration'] = map(lambda x:'{:%Mmin %Ssec}'.format(x),df['duration'])
    
    df['start_as_text'] = map(lambda x: arrow.get(x).humanize(), df['start'])

from hyperstream import HyperStream, StreamId, TimeInterval, UTC
    from workflows.display_experiments import create_workflow_list_technicians_walkarounds
    from workflows.deploy_localisation_model import create_workflow_localisation_predict

    hyperstream = HyperStream(loglevel=loglevel)
    M = hyperstream.channel_manager.memory

    workflow_id0 = "list_technicians_walkarounds"

    if delete_existing_workflows:
        hyperstream.workflow_manager.delete_workflow(workflow_id0)

    try:
        w0 = hyperstream.workflow_manager.workflows[workflow_id0]
    except KeyError:
        w0 = create_workflow_list_technicians_walkarounds(hyperstream, house=house, safe=False)
        hyperstream.workflow_manager.commit_workflow(workflow_id0)
    time_interval = TimeInterval.up_to_now()
    w0.execute(time_interval)

    df = M[StreamId('experiments_dataframe', dict(house=house))].window(TimeInterval.up_to_now()).values()[0]
    experiment_ids = set([df['experiment_id'][i - 1] for i in selection])

    hyperstream.plate_manager.delete_plate("H.SelectedLocalisationExperiment")
    hyperstream.plate_manager.create_plate(
        plate_id="H.SelectedLocalisationExperiment",
        description="Localisation experiments selected by the technician in SPHERE house",
        meta_data_id="localisation-experiment",
        values=[],
        complement=True,
        parent_plate="H"
    )

from hyperstream.utils import StreamNotFoundError

    hyperstream = HyperStream(loglevel=logging.INFO)
    M = hyperstream.channel_manager.memory
    D = hyperstream.channel_manager.mongo
    A = hyperstream.channel_manager.assets

    workflow_id0 = "list_technicians_walkarounds"

    if delete_existing_workflows:
        hyperstream.workflow_manager.delete_workflow(workflow_id0)

    try:
        w0 = hyperstream.workflow_manager.workflows[workflow_id0]
    except KeyError:
        w0 = create_workflow_list_technicians_walkarounds(hyperstream, house=house, safe=False)
        hyperstream.workflow_manager.commit_workflow(workflow_id0)
    time_interval = TimeInterval.up_to_now()
    w0.execute(time_interval)

    df = M[StreamId('experiments_dataframe', dict(house=house))].window(TimeInterval.up_to_now()).values()[0]
    experiment_ids = set([df['experiment_id'][i - 1] for i in selection])

    hyperstream.plate_manager.delete_plate("H.SelectedLocalisationExperiment")
    hyperstream.plate_manager.create_plate(
        plate_id="H.SelectedLocalisationExperiment",
        description="Localisation experiments selected by the technician in SPHERE house",
        meta_data_id="localisation-experiment",
        values=[],
        complement=True,
        parent_plate="H"
    )

from workflows.display_experiments import create_workflow_list_technicians_walkarounds
    from workflows.rssi_distributions_per_room import create_workflow_rssi_distributions_per_room
    from hyperstream.utils import StreamNotFoundError

    hyperstream = HyperStream(loglevel=logging.INFO)
    M = hyperstream.channel_manager.memory

    workflow_id0 = "list_technicians_walkarounds"

    if delete_existing_workflows:
        hyperstream.workflow_manager.delete_workflow(workflow_id0)

    try:
        w0 = hyperstream.workflow_manager.workflows[workflow_id0]
    except KeyError:
        w0 = create_workflow_list_technicians_walkarounds(hyperstream, house=house, safe=False)
        hyperstream.workflow_manager.commit_workflow(workflow_id0)
    time_interval = TimeInterval.up_to_now()
    w0.execute(time_interval)

    df = M[StreamId('experiments_dataframe', dict(house=house))].window(TimeInterval.up_to_now()).values()[0]
    experiment_indices = selection
    experiment_ids = set([df['experiment_id'][i - 1] for i in selection])

    hyperstream.plate_manager.delete_plate("H.SelectedLocalisationExperiment")
    hyperstream.plate_manager.create_plate(
        plate_id="H.SelectedLocalisationExperiment",
        description="Localisation experiments selected by the technician in SPHERE house",
        meta_data_id="localisation-experiment",
        values=[],
        complement=True,
        parent_plate="H"