Creative Layouts - Multiple Ward Example

import pandas as pd

from examples.example_9_wide_resource_spacing_multiple.ex_9_model_classes import g, Trial

from vidigi.animation import animate_activity_log

import plotly.io as pio
pio.renderers.default = "notebook"

clinic_simulation = Trial()

clinic_simulation.trial_results

	entity_id	event_type	event	time	pathway	run_number	timestamp	resource_id
0	1	arrival_departure	arrival	0.000000	None	1	None	NaN
1	1	queue	bed_wait_begins	0.000000	None	1	None	NaN
2	1	resource_use	maple_stay_begins	0.000000	None	1	None	1.0
3	2	arrival_departure	arrival	0.519445	None	1	None	NaN
4	2	queue	bed_wait_begins	0.519445	None	1	None	NaN
...	...	...	...	...	...	...	...	...
10272	2143	arrival_departure	arrival	8735.445825	None	100	None	NaN
10273	2143	queue	bed_wait_begins	8735.445825	None	100	None	NaN
10274	2144	arrival_departure	arrival	8735.740369	None	100	None	NaN
10275	2144	queue	bed_wait_begins	8735.740369	None	100	None	NaN
10276	2144	resource_use	maple_stay_begins	8735.740369	None	100	None	8.0

1050654 rows × 8 columns

event_position_df = pd.DataFrame([
                    {'event': 'arrival',
                     'x':  50, 'y': 800,
                     'label': "Arrival" },

                    # Triage - minor and trauma
                    {'event': 'bed_wait_begins',
                     'x':  505, 'y': 700,
                     'label': "Waiting for Bed<br>in Preferred Ward"},

                    {'event': 'ash_stay_begins',
                     'x':  675, 'y': 275,
                     'resource':'number_of_beds_ash',
                     'label': "Ash Ward"},

                    {'event': 'oak_stay_begins',
                     'x':  205, 'y': 475,
                     'resource':'number_of_beds_oak',
                     'label': "Oak Ward"},

                    {'event': 'maple_stay_begins',
                     'x':  205, 'y': 175,
                     'resource':'number_of_beds_maple',
                     'label': "Maple Ward"},

                    {'event': 'depart',
                     'x':  740, 'y': 70,
                     'label': "Exit"}

                ])

animate_activity_log(
        event_log=clinic_simulation.trial_results[clinic_simulation.trial_results['run_number']==1],
        event_position_df= event_position_df,
        scenario=g(),
        # Key animation prep parameters
        every_x_time_units=3,
        simulation_time_unit="hours",
        limit_duration=g.sim_duration,
        step_snapshot_max=125,
        # Animation display parameters
        time_display_units="dhm",
        include_play_button=True,
        setup_mode=False,
        debug_mode=True,
        frame_duration=500,
        # Text parameters
        display_stage_labels=True,
        text_size=20,
        # Entity and queue size and spacing
        entity_icon_size=16,
        wrap_queues_at=25,
        gap_between_entities=12,
        gap_between_queue_rows=30,
        # Resource size and spacing
        gap_between_resources=80,
        gap_between_resource_rows=40,
        resource_icon_size=25,
        wrap_resources_at=2,
        custom_resource_icon='🛏️',
        custom_entity_icon_list=['🧍'],
        # Plot size
        plotly_height=800,
        plotly_width=1000,
        # Internal plot coordinates
        override_x_max=800,
        override_y_max=900,
        )

Animation function called at 10:00:28
Iteration through time-unit-by-time-unit logs complete 10:00:47
Snapshot df concatenation complete at 10:00:48
Reshaped animation dataframe finished construction at 10:00:49
Placement dataframe finished construction at 10:00:50
Output animation generation complete at 10:01:07
Total Time Elapsed: 39.66 seconds