Example 4: A ciw Model

Note that this example is written using ciw 2.x

It will not run with 3.x - but could theoretically be adapted to do so

The ‘logs’ object is the result of running

sim_engine.get_all_records()

However, note that while we run multiple replications, we only pass the records for a single replication to the event_log_from_ciw_recs function.

The underlying model code is from Monks, T., Harper, A., & Heather, A. (2023). Towards Sharing Tools, Artefacts, and Reproducible Simulation: a ciw model example (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.10051494

See here for the adaptation embedded within that repo: https://github.com/Bergam0t/ciw-example-animation/tree/main

import pandas as pd
# Import the wrapper objects for model interaction.
from examples.example_4_ciw.ex_4_ciw_model import Experiment, multiple_replications
from vidigi.utils import event_log_from_ciw_recs
from vidigi.animation import animate_activity_log

N_OPERATORS = 13
N_NURSES = 9
RESULTS_COLLECTION_PERIOD = 1000

user_experiment = Experiment(n_operators=N_OPERATORS,
                             n_nurses=N_NURSES,
                             chance_callback=0.4)

# run multiple replications
results, logs = multiple_replications(user_experiment, n_reps=10)

# the 'logs' object contains a list, where each entry is the recs object for that run
logs_run_1 = logs[0]

print(len(logs_run_1))

# let's print all of the outputs for a single individual
[print(log) for log in logs_run_1 if log.id_number==500]

# let's now try turning this into an event log
event_log_test = event_log_from_ciw_recs(logs_run_1, node_name_list=["operator", "nurse"])

event_log_test.head(25)

Record(id_number=500, customer_class=0, original_customer_class=0, node=1, arrival_date=269.6165843237451, waiting_time=14.514495676004913, service_start_date=284.13107999975, service_time=8.289105946048721, service_end_date=292.4201859457987, time_blocked=0.0, exit_date=292.4201859457987, destination=-1, queue_size_at_arrival=37, queue_size_at_departure=43, server_id=1, record_type='service')

	patient	pathway	event_type	event	time	resource_id
0	1	Model	arrival_departure	arrival	0.503707	NaN
1	1	Model	queue	operator_wait_begins	0.503707	NaN
2	1	Model	resource_use	operator_begins	0.503707	1.0
3	1	Model	resource_use	operator_ends	6.136387	1.0
4	1	Model	queue	nurse_wait_begins	6.136387	NaN
5	1	Model	resource_use	nurse_begins	6.136387	1.0
6	1	Model	resource_use	nurse_ends	23.061561	1.0
7	1	Model	arrival_departure	depart	23.061561	NaN
8	2	Model	arrival_departure	arrival	1.048958	NaN
9	2	Model	queue	operator_wait_begins	1.048958	NaN
10	2	Model	resource_use	operator_begins	1.048958	2.0
11	2	Model	resource_use	operator_ends	8.075635	2.0
12	2	Model	arrival_departure	depart	8.075635	NaN
13	3	Model	arrival_departure	arrival	1.392097	NaN
14	3	Model	queue	operator_wait_begins	1.392097	NaN
15	3	Model	resource_use	operator_begins	1.392097	3.0
16	3	Model	resource_use	operator_ends	8.482409	3.0
17	3	Model	queue	nurse_wait_begins	8.482409	NaN
18	3	Model	resource_use	nurse_begins	8.482409	2.0
19	3	Model	resource_use	nurse_ends	19.488025	2.0
20	3	Model	arrival_departure	depart	19.488025	NaN
21	4	Model	arrival_departure	arrival	2.280472	NaN
22	4	Model	queue	operator_wait_begins	2.280472	NaN
23	4	Model	resource_use	operator_begins	2.280472	4.0
24	4	Model	resource_use	operator_ends	10.076369	4.0

# Create required event_position_df for vidigi animation
event_position_df = pd.DataFrame([
                    {'event': 'arrival',
                     'x':  30, 'y': 350,
                     'label': "Arrival"},

                    {'event': 'operator_wait_begins',
                     'x':  205, 'y': 270,
                     'label': "Waiting for Operator"},

                    {'event': 'operator_begins',
                     'x':  210, 'y': 210,
                     'resource':'n_operators',
                     'label': "Speaking to operator"},

                    {'event': 'nurse_wait_begins',
                     'x':  205, 'y': 110,
                     'label': "Waiting for Nurse"},

                    {'event': 'nurse_begins',
                     'x':  210, 'y': 50,
                     'resource':'n_nurses',
                     'label': "Speaking to Nurse"},

                    {'event': 'exit',
                     'x':  270, 'y': 10,
                     'label': "Exit"}

                ])

# Create a suitable class to pass in the resource numbers
class model_params():
    n_operators = N_OPERATORS
    n_nurses = N_NURSES

# Create animation
animate_activity_log(
        event_log=event_log_test,
        event_position_df= event_position_df,
        scenario=model_params(),
        debug_mode=True,
        setup_mode=False,
        every_x_time_units=1,
        include_play_button=True,
        icon_and_text_size=20,
        gap_between_entities=8,
        gap_between_rows=25,
        plotly_height=700,
        frame_duration=200,
        plotly_width=1200,
        override_x_max=300,
        override_y_max=300,
        limit_duration=RESULTS_COLLECTION_PERIOD,
        wrap_queues_at=25,
        step_snapshot_max=75,
        time_display_units="dhm",
        display_stage_labels=True,
    )

Animation function called at 18:07:36
Iteration through minute-by-minute logs complete 18:07:40
Snapshot df concatenation complete at 18:07:40
Reshaped animation dataframe finished construction at 18:07:40
Placement dataframe finished construction at 18:07:40
Output animation generation complete at 18:07:44
Total Time Elapsed: 7.97 seconds

Unable to display output for mime type(s): application/vnd.plotly.v1+json