Animation of a SimPy Model with Branching and Multiple Steps

from ex_2_model_classes import Trial, g

from vidigi.animation import animate_activity_log
from vidigi.utils import EventPosition, create_event_position_df

NoteView Imported Code, which has had logging steps added at the appropriate points in the ‘model’ class

import random

import numpy as np
import pandas as pd
import simpy
from sim_tools.distributions import Bernoulli, Exponential, Lognormal, Normal, Uniform

from vidigi.resources import VidigiStore
from vidigi.logging import EventLogger, TrialLogger

TRACE = False


def trace(msg, show=TRACE):
    """
    Utility function for printing a trace as the
    simulation model executes.
    Set the TRACE constant to False, to turn tracing off.

    Params:
    -------
    msg: str
        string to print to screen.
    """
    if show:
        print(msg)


# Class to store global parameter values.  We don't create an instance of this
# class - we just refer to the class blueprint itself to access the numbers
# inside.
class g:
    """
    Create a scenario to parameterise the simulation model

    Parameters:
    -----------
    random_number_set: int, optional (default=DEFAULT_RNG_SET)
        Set to control the initial seeds of each stream of pseudo
        random numbers used in the model.

    n_triage: int
        The number of triage cubicles

    n_reg: int
        The number of registration clerks

    n_exam: int
        The number of examination rooms

    n_trauma: int
        The number of trauma bays for stablisation

    n_cubicles_non_trauma_treat: int
        The number of non-trauma treatment cubicles

    n_cubicles_trauma_treat: int
        The number of trauma treatment cubicles

    triage_mean: float
        Mean duration of the triage distribution (Exponential)

    reg_mean: float
        Mean duration of the registration distribution (Lognormal)

    reg_var: float
        Variance of the registration distribution (Lognormal)

    exam_mean: float
        Mean of the examination distribution (Normal)

    exam_var: float
        Variance of the examination distribution (Normal)

    trauma_mean: float
        Mean of the trauma stabilisation distribution (Exponential)

    trauma_treat_mean: float
        Mean of the trauma cubicle treatment distribution (Lognormal)

    trauma_treat_var: float
        Variance of the trauma cubicle treatment distribution (Lognormal)

    non_trauma_treat_mean: float
        Mean of the non trauma treatment distribution

    non_trauma_treat_var: float
        Variance of the non trauma treatment distribution

    non_trauma_treat_p: float
        Probability non trauma patient requires treatment

    prob_trauma: float
        probability that a new arrival is a trauma patient.
    """

    random_number_set = 42

    n_triage = 2
    n_reg = 2
    n_exam = 3
    n_trauma = 4
    n_cubicles_non_trauma_treat = 4
    n_cubicles_trauma_treat = 5

    triage_mean = 6
    reg_mean = 8
    reg_var = 2
    exam_mean = 16
    exam_var = 3
    trauma_mean = 90
    trauma_treat_mean = 30
    trauma_treat_var = 4
    non_trauma_treat_mean = 13.3
    non_trauma_treat_var = 2

    non_trauma_treat_p = 0.6
    prob_trauma = 0.12

    arrival_df = "ed_arrivals.csv"

    sim_duration = 600
    number_of_runs = 100


# Class representing patients coming in to the clinic.
class Patient:
    """
    Class defining details for a patient entity
    """

    def __init__(self, p_id):
        """
        Constructor method

        Params:
        -----
        identifier: int
            a numeric identifier for the patient.
        """
        self.identifier = p_id

        # Time of arrival in model/at centre
        self.arrival = -np.inf
        # Total time in pathway
        self.total_time = -np.inf

        # Shared waits
        self.wait_triage = -np.inf
        self.wait_reg = -np.inf
        self.wait_treat = -np.inf
        # Non-trauma pathway - examination wait
        self.wait_exam = -np.inf
        # Trauma pathway - stabilisation wait
        self.wait_trauma = -np.inf

        # Shared durations
        self.triage_duration = -np.inf
        self.reg_duration = -np.inf
        self.treat_duration = -np.inf

        # Non-trauma pathway - examination duration
        self.exam_duration = -np.inf
        # Trauma pathway - stabilisation duration
        self.trauma_duration = -np.inf


# Class representing our model of the clinic.
class Model:
    """
    Simulates the simplest minor treatment process for a patient

    1. Arrive
    2. Examined/treated by nurse when one available
    3. Discharged
    """

    # Constructor to set up the model for a run.  We pass in a run number when
    # we create a new model.
    def __init__(self, run_number):
        # Create a SimPy environment in which everything will live
        self.env = simpy.Environment()

        self.event_log = []

        # Create a patient counter (which we'll use as a patient ID)
        self.patient_counter = 0

        self.trauma_patients = []
        self.non_trauma_patients = []

        # Store the passed in run number
        self.run_number = run_number

        self.event_log = EventLogger(env=self.env, run_number=self.run_number)

        # Create our resources
        self.init_resources()

        # Create a new Pandas DataFrame that will store some results against
        # the patient ID (which we'll use as the index).
        self.results_df = pd.DataFrame()
        self.results_df["Patient ID"] = [1]
        self.results_df["Queue Time Cubicle"] = [0.0]
        self.results_df["Time with Nurse"] = [0.0]
        self.results_df.set_index("Patient ID", inplace=True)

        # Create an attribute to store the mean queuing times across this run of
        # the model
        self.mean_q_time_cubicle = 0

        # create distributions

        # Triage duration
        self.triage_dist = Exponential(
            g.triage_mean, random_seed=self.run_number * g.random_number_set
        )

        # Registration duration (non-trauma only)
        self.reg_dist = Lognormal(
            g.reg_mean,
            np.sqrt(g.reg_var),
            random_seed=self.run_number * g.random_number_set,
        )

        # Evaluation (non-trauma only)
        self.exam_dist = Normal(
            g.exam_mean,
            np.sqrt(g.exam_var),
            random_seed=self.run_number * g.random_number_set,
        )

        # Trauma/stablisation duration (trauma only)
        self.trauma_dist = Exponential(
            g.trauma_mean, random_seed=self.run_number * g.random_number_set
        )

        # Non-trauma treatment
        self.nt_treat_dist = Lognormal(
            g.non_trauma_treat_mean,
            np.sqrt(g.non_trauma_treat_var),
            random_seed=self.run_number * g.random_number_set,
        )

        # treatment of trauma patients
        self.treat_dist = Lognormal(
            g.trauma_treat_mean,
            np.sqrt(g.non_trauma_treat_var),
            random_seed=self.run_number * g.random_number_set,
        )

        # probability of non-trauma patient requiring treatment
        self.nt_p_treat_dist = Bernoulli(
            g.non_trauma_treat_p, random_seed=self.run_number * g.random_number_set
        )

        # probability of non-trauma versus trauma patient
        self.p_trauma_dist = Bernoulli(
            g.prob_trauma, random_seed=self.run_number * g.random_number_set
        )

        # init sampling for non-stationary poisson process
        self.init_nspp()

    def init_nspp(self):
        # read arrival profile
        self.arrivals = pd.read_csv(g.arrival_df)  # pylint: disable=attribute-defined-outside-init
        self.arrivals["mean_iat"] = 60 / self.arrivals["arrival_rate"]

        # maximum arrival rate (smallest time between arrivals)
        self.lambda_max = self.arrivals["arrival_rate"].max()  # pylint: disable=attribute-defined-outside-init

        # thinning exponential
        self.arrival_dist = Exponential(
            60.0 / self.lambda_max,  # pylint: disable=attribute-defined-outside-init
            random_seed=self.run_number * g.random_number_set,
        )

        # thinning uniform rng
        self.thinning_rng = Uniform(
            low=0.0,
            high=1.0,  # pylint: disable=attribute-defined-outside-init
            random_seed=self.run_number * g.random_number_set,
        )

    def init_resources(self):
        """
        Init the number of resources
        and store in the arguments container object

        Resource list:
            1. Nurses/treatment bays (same thing in this model)

        """
        # Shared Resources
        self.triage_cubicles = VidigiStore(
            self.env, num_resources=g.n_triage, label="triage", logger=self.event_log
        )

        self.registration_cubicles = VidigiStore(
            self.env, num_resources=g.n_reg, label="registration", logger=self.event_log
        )

        # Non-trauma
        self.exam_cubicles = VidigiStore(self.env, num_resources=g.n_exam, label="exam", logger=self.event_log)

        self.non_trauma_treatment_cubicles = VidigiStore(
            self.env,
            num_resources=g.n_cubicles_non_trauma_treat,
            label="non_trauma_treatment", logger=self.event_log
        )

        # Trauma
        self.trauma_stabilisation_bays = VidigiStore(
            self.env, num_resources=g.n_trauma, label="trauma_stabilisation", logger=self.event_log
        )

        self.trauma_treatment_cubicles = VidigiStore(
            self.env, num_resources=g.n_cubicles_trauma_treat, label="trauma_treatment", logger=self.event_log
        )

    # A generator function that represents the DES generator for patient
    # arrivals
    def generator_patient_arrivals(self):
        # We use an infinite loop here to keep doing this indefinitely whilst
        # the simulation runs
        while True:
            t = int(self.env.now // 60) % self.arrivals.shape[0]
            lambda_t = self.arrivals["arrival_rate"].iloc[t]

            # set to a large number so that at least 1 sample taken!
            u = np.inf

            interarrival_time = 0.0
            # reject samples if u >= lambda_t / lambda_max
            while u >= (lambda_t / self.lambda_max):
                interarrival_time += self.arrival_dist.sample()
                u = self.thinning_rng.sample()

            # Freeze this instance of this function in place until the
            # inter-arrival time we sampled above has elapsed.  Note - time in
            # SimPy progresses in "Time Units", which can represent anything
            # you like (just make sure you're consistent within the model)
            yield self.env.timeout(interarrival_time)

            # Increment the patient counter by 1 (this means our first patient
            # will have an ID of 1)
            self.patient_counter += 1

            # Create a new patient - an instance of the Patient Class we
            # defined above.  Remember, we pass in the ID when creating a
            # patient - so here we pass the patient counter to use as the ID.
            p = Patient(self.patient_counter)

            trace(f"patient {self.patient_counter} arrives at: {self.env.now:.3f}")

            # sample if the patient is trauma or non-trauma
            trauma = self.p_trauma_dist.sample()

            # Tell SimPy to start up the attend_clinic generator function with
            # this patient (the generator function that will model the
            # patient's journey through the system)
            # and store patient in list for later easy access
            if trauma:
                # create and store a trauma patient to update KPIs.
                self.trauma_patients.append(p)
                self.env.process(self.attend_trauma_pathway(p))

            else:
                # create and store a non-trauma patient to update KPIs.
                self.non_trauma_patients.append(p)
                self.env.process(self.attend_non_trauma_pathway(p))

    # A generator function that represents the pathway for a patient going
    # through the clinic.
    # The patient object is passed in to the generator function so we can
    # extract information from / record information to it
    def attend_non_trauma_pathway(self, patient):
        """
        simulates the non-trauma/minor treatment process for a patient

        1. request and wait for sign-in/triage
        2. patient registration
        3. examination
        4a. percentage discharged
        4b. remaining percentage treatment then discharge
        """
        # record the time of arrival and entered the triage queue
        patient.arrival = self.env.now
        self.event_log.log_arrival(entity_id=patient.identifier, pathway="Non-Trauma")
        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Non-Trauma",
            event="triage_wait_begins"
            )

        ###################################################
        # request sign-in/triage
        with self.triage_cubicles.request(entity_id=patient.identifier, pathway="Non-Trauma") as req:
            yield req

            # record the waiting time for triage
            patient.wait_triage = self.env.now - patient.arrival
            trace(f"patient {patient.identifier} triaged to minors {self.env.now:.3f}")

            # sample triage duration.
            patient.triage_duration = self.triage_dist.sample()
            yield self.env.timeout(patient.triage_duration)

            trace(
                f"triage {patient.identifier} complete {self.env.now:.3f}; "
                f"waiting time was {patient.wait_triage:.3f}"
            )

        #########################################################

        # record the time that entered the registration queue
        start_wait = self.env.now
        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Non-Trauma",
            event="MINORS_registration_wait_begins"
            )

        #########################################################
        # request registration clerk
        with self.registration_cubicles.request(entity_id=patient.identifier, pathway="Non-Trauma") as req:
            yield req

            # record the waiting time for registration
            patient.wait_reg = self.env.now - start_wait
            trace(f"registration of patient {patient.identifier} at {self.env.now:.3f}")

            # sample registration duration.
            patient.reg_duration = self.reg_dist.sample()
            yield self.env.timeout(patient.reg_duration)

            trace(
                f"patient {patient.identifier} registered at"
                f"{self.env.now:.3f}; "
                f"waiting time was {patient.wait_reg:.3f}"
            )

            ########################################################

        # record the time that entered the evaluation queue
        start_wait = self.env.now

        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Non-Trauma",
            event="MINORS_examination_wait_begins"
        )

        #########################################################
        # request examination resource
        with self.exam_cubicles.request(entity_id=patient.identifier, pathway="Non-Trauma") as req:
            yield req

            # record the waiting time for examination to begin
            patient.wait_exam = self.env.now - start_wait
            trace(f"examination of patient {patient.identifier} begins {self.env.now:.3f}")

            # sample examination duration.
            patient.exam_duration = self.exam_dist.sample()
            yield self.env.timeout(patient.exam_duration)

            trace(
                f"patient {patient.identifier} examination complete "
                f"at {self.env.now:.3f};"
                f"waiting time was {patient.wait_exam:.3f}"
            )
            ############################################################################

        # sample if patient requires treatment?
        patient.require_treat = self.nt_p_treat_dist.sample()  # pylint: disable=attribute-defined-outside-init

        if patient.require_treat:
            self.event_log.log_custom_event(
                entity_id=patient.identifier,
                event_type="attribute_assigned",
                event="requires_treatment",
                pathway="Non-Trauma"
                )

            # record the time that entered the treatment queue
            start_wait = self.env.now
            self.event_log.log_queue(
                        entity_id=patient.identifier,
                        pathway="Non-Trauma",
                        event="MINORS_treatment_wait_begins"
                    )

            ###################################################
            # request treatment cubicle

            with self.non_trauma_treatment_cubicles.request(entity_id=patient.identifier, pathway="Non-Trauma") as req:
                yield req
                # record the waiting time for treatment
                patient.wait_treat = self.env.now - start_wait
                trace(
                    f"treatment of patient {patient.identifier} begins {self.env.now:.3f}"
                )

                # sample treatment duration.
                patient.treat_duration = self.nt_treat_dist.sample()
                yield self.env.timeout(patient.treat_duration)

                trace(
                    f"patient {patient.identifier} treatment complete "
                    f"at {self.env.now:.3f};"
                    f"waiting time was {patient.wait_treat:.3f}"
                )

        ##########################################################################

        # Return to what happens to all patients, regardless of whether they were sampled as needing treatment
        self.event_log.log_departure(entity_id=patient.identifier, pathway="Trauma")

        # total time in system
        patient.total_time = self.env.now - patient.arrival

    def attend_trauma_pathway(self, patient):
        """
        simulates the major treatment process for a patient

        1. request and wait for sign-in/triage
        2. trauma
        3. treatment
        """
        # record the time of arrival and entered the triage queue
        patient.arrival = self.env.now
        self.event_log.log_arrival(entity_id=patient.identifier, pathway="Trauma")
        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Trauma",
            event="triage_wait_begins"
            )

        ###################################################
        # request sign-in/triage
        with self.triage_cubicles.request(entity_id=patient.identifier, pathway="Trauma") as req:
            yield req

            # record the waiting time for triage
            patient.wait_triage = self.env.now - patient.arrival

            trace(f"patient {patient.identifier} triaged to trauma {self.env.now:.3f}")

            # sample triage duration.
            patient.triage_duration = self.triage_dist.sample()
            yield self.env.timeout(patient.triage_duration)

            trace(
                f"triage {patient.identifier} complete {self.env.now:.3f}; "
                f"waiting time was {patient.wait_triage:.3f}"
            )

            ###################################################

        # record the time that entered the trauma queue
        start_wait = self.env.now
        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Trauma",
            event="TRAUMA_stabilisation_wait_begins"
            )

        ###################################################
        # request trauma room
        with self.trauma_stabilisation_bays.request(entity_id=patient.identifier, pathway="Trauma") as req:
            yield req

            # record the waiting time for trauma
            patient.wait_trauma = self.env.now - start_wait

            # sample stablisation duration.
            patient.trauma_duration = self.trauma_dist.sample()
            yield self.env.timeout(patient.trauma_duration)

            trace(f"stabilisation of patient {patient.identifier} begins at {self.env.now}")

            #######################################################

        # record the time that patient entered the treatment queue
        start_wait = self.env.now
        self.event_log.log_queue(
            entity_id=patient.identifier,
            pathway="Trauma",
            event="TRAUMA_treatment_wait_begins"
            )

        ########################################################
        # request treatment cubicle
        with self.trauma_treatment_cubicles.request(entity_id=patient.identifier, pathway="Trauma") as req:
            yield req

            # record the waiting time for trauma
            patient.wait_treat = self.env.now - start_wait
            trace(f"treatment of patient {patient.identifier} at {self.env.now:.3f}")

            # sample treatment duration.
            patient.treat_duration = self.trauma_dist.sample()
            yield self.env.timeout(patient.treat_duration)

            trace(
                f"patient {patient.identifier} treatment complete {self.env.now:.3f}; "
                f"waiting time was {patient.wait_treat:.3f}"
            )

        #########################################################

        self.event_log.log_departure(entity_id=patient.identifier, pathway="Trauma")

        # total time in system
        patient.total_time = self.env.now - patient.arrival

    # This method calculates results over a single run.  Here we just calculate
    # a mean, but in real world models you'd probably want to calculate more.
    def calculate_run_results(self):
        # Take the mean of the queuing times across patients in this run of the
        # model.
        self.mean_q_time_cubicle = self.results_df["Queue Time Cubicle"].mean()

    # The run method starts up the DES entity generators, runs the simulation,
    # and in turns calls anything we need to generate results for the run
    def run(self):
        # Start up our DES entity generators that create new patients.  We've
        # only got one in this model, but we'd need to do this for each one if
        # we had multiple generators.
        self.env.process(self.generator_patient_arrivals())

        # Run the model for the duration specified in g class
        self.env.run(until=g.sim_duration)

        # Now the simulation run has finished, call the method that calculates
        # run results

        return self.results_df


# Class representing a Trial for our simulation - a batch of simulation runs.
class Trial:
    # The constructor sets up a pandas dataframe that will store the key
    # results from each run against run number, with run number as the index.
    def __init__(self):
        self.df_trial_results = pd.DataFrame()
        self.df_trial_results["Run Number"] = [0]
        self.df_trial_results["Arrivals"] = [0]
        self.df_trial_results["Mean Queue Time Cubicle"] = [0.0]
        self.df_trial_results.set_index("Run Number", inplace=True)

        self.trial_logger = TrialLogger()

    # Method to run a trial
    def run_trial(self):
        # Run the simulation for the number of runs specified in g class.
        # For each run, we create a new instance of the Model class and call its
        # run method, which sets everything else in motion.  Once the run has
        # completed, we grab out the stored run results (just mean queuing time
        # here) and store it against the run number in the trial results
        # dataframe.
        for run in range(g.number_of_runs):
            random.seed(run)

            my_model = Model(run)
            patient_level_results = my_model.run()

            self.df_trial_results.loc[run] = [
                len(patient_level_results),
                my_model.mean_q_time_cubicle,
            ]

            self.trial_logger.add_log(event_log=my_model.event_log)

my_trial = Trial()

my_trial.run_trial()

Let’s take a look at some sample logs from a single run.

my_trial.trial_logger.get_log_by_run(run=1, as_df=True).head(20)

	entity_id	event_type	event	time	pathway	run_number	resource_id	unique_resource_id
0	1	arrival_departure	arrival	37.593555	Non-Trauma	1	NaN	NaN
1	1	queue	triage_wait_begins	37.593555	Non-Trauma	1	NaN	NaN
2	1	resource_use	triage_start	37.593555	Non-Trauma	1	1.0	triage_1
3	2	arrival_departure	arrival	51.835879	Non-Trauma	1	NaN	NaN
4	2	queue	triage_wait_begins	51.835879	Non-Trauma	1	NaN	NaN
5	2	resource_use	triage_start	51.835879	Non-Trauma	1	2.0	triage_2
6	1	resource_use_end	triage_end	52.018807	Non-Trauma	1	1.0	triage_1
7	1	queue	MINORS_registration_wait_begins	52.018807	Non-Trauma	1	NaN	NaN
8	1	resource_use	registration_start	52.018807	Non-Trauma	1	1.0	registration_1
9	1	resource_use_end	registration_end	60.329215	Non-Trauma	1	1.0	registration_1
10	1	queue	MINORS_examination_wait_begins	60.329215	Non-Trauma	1	NaN	NaN
11	1	resource_use	exam_start	60.329215	Non-Trauma	1	1.0	exam_1
12	2	resource_use_end	triage_end	65.853017	Non-Trauma	1	2.0	triage_2
13	2	queue	MINORS_registration_wait_begins	65.853017	Non-Trauma	1	NaN	NaN
14	2	resource_use	registration_start	65.853017	Non-Trauma	1	2.0	registration_2
15	2	resource_use_end	registration_end	72.417168	Non-Trauma	1	2.0	registration_2
16	2	queue	MINORS_examination_wait_begins	72.417168	Non-Trauma	1	NaN	NaN
17	2	resource_use	exam_start	72.417168	Non-Trauma	1	2.0	exam_2
18	1	resource_use_end	exam_end	76.857001	Non-Trauma	1	1.0	exam_1
19	1	arrival_departure	depart	76.857001	Trauma	1	NaN	NaN

event_position_df = create_event_position_df(
    [
        EventPosition(event="arrival", x=10, y=250, label="Arrival"),
        # Triage - minor and trauma
        EventPosition(
            event="triage_wait_begins", x=160, y=375, label="Waiting for<br>Triage"
        ),
        EventPosition(
            event='triage_start',
            x=160,
            y=315,
            resource="n_triage",
            label="Being Triaged",
        ),
        # Minors (non-trauma) pathway
        EventPosition(
            event="MINORS_registration_wait_begins",
            x=300,
            y=145,
            label="Waiting for<br>Registration",
        ),
        EventPosition(
            event="registration_start",
            x=300,
            y=85,
            resource="n_reg",
            label="Being<br>Registered",
        ),
        EventPosition(
            event="MINORS_examination_wait_begins",
            x=465,
            y=145,
            label="Waiting for<br>Examination",
        ),
        EventPosition(
            event='exam_start',
            x=465,
            y=85,
            resource="n_exam",
            label="Being<br>Examined",
        ),
        EventPosition(
            event="MINORS_treatment_wait_begins",
            x=630,
            y=145,
            label="Waiting for<br>Treatment",
        ),
        EventPosition(
            event='non_trauma_treatment_start',
            x=630,
            y=85,
            resource="n_cubicles_non_trauma_treat",
            label="Being<br>Treated",
        ),
        # Trauma pathway
        EventPosition(
            event="TRAUMA_stabilisation_wait_begins",
            x=300,
            y=560,
            label="Waiting for<br>Stabilisation",
        ),
        EventPosition(
            event='trauma_stabilisation_start',
            x=300,
            y=490,
            resource="n_trauma",
            label="Being<br>Stabilised",
        ),
        EventPosition(
            event="TRAUMA_treatment_wait_begins",
            x=630,
            y=560,
            label="Waiting for<br>Treatment",
        ),
        EventPosition(
            event='trauma_treatment_start',
            x=630,
            y=490,
            resource="n_cubicles_trauma_treat",
            label="Being<br>Treated",
        ),
        EventPosition(event="depart", x=670, y=330, label="Exit"),
    ]
)

setup_mode = True

setup_mode allows us to see how the coordinates of our plot relate to the positioning of our background image, allowing us to more accurately place our entities.

animate_activity_log(
    event_log=my_trial.trial_logger,
    run_number=1,
    event_position_df=event_position_df,
    scenario=g(),
    debug_mode=True,
    setup_mode=True,
    every_x_time_units=5,
    include_play_button=True,
    gap_between_entities=11,
    gap_between_resources=15,
    gap_between_resource_rows=30,
    gap_between_queue_rows=30,
    plotly_height=600,
    plotly_width=1000,
    override_x_max=700,
    override_y_max=675,
    entity_icon_size=10,
    resource_icon_size=13,
    text_size=15,
    wrap_queues_at=10,
    step_snapshot_max=20,
    limit_duration=g.sim_duration,
    time_display_units="dhm",
    display_stage_labels=False,
    add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_2_branching_multistep/Full%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
)

Animation function called at 13:53:19
Iteration through time-unit-by-time-unit logs complete 13:53:19
Snapshot df concatenation complete at 13:53:20
Reshaped animation dataframe finished construction at 13:53:20
Placement dataframe started construction at 13:53:20
Placement dataframe finished construction at 13:53:20
Output animation generation complete at 13:53:21
Total Time Elapsed: 2.30 seconds

setup_mode = False

We can then rerun our plot with setup_mode=False to remove the grid lines and axis tick marks.

animate_activity_log(
    event_log=my_trial.trial_logger,
    run_number=1,
    event_position_df=event_position_df,
    scenario=g(),
    debug_mode=True,
    setup_mode=False,
    every_x_time_units=5,
    include_play_button=True,
    gap_between_entities=11,
    gap_between_resources=15,
    gap_between_resource_rows=30,
    gap_between_queue_rows=30,
    plotly_height=600,
    plotly_width=1000,
    override_x_max=700,
    override_y_max=675,
    entity_icon_size=10,
    resource_icon_size=13,
    text_size=15,
    wrap_queues_at=10,
    step_snapshot_max=20,
    limit_duration=g.sim_duration,
    time_display_units="dhm",
    display_stage_labels=False,
    add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_2_branching_multistep/Full%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
)

Animation function called at 13:53:21
Iteration through time-unit-by-time-unit logs complete 13:53:22
Snapshot df concatenation complete at 13:53:22
Reshaped animation dataframe finished construction at 13:53:22
Placement dataframe started construction at 13:53:22
Placement dataframe finished construction at 13:53:22
Output animation generation complete at 13:53:23
Total Time Elapsed: 2.25 seconds

Visualising long queues

Finally, let’s rerun this with some build-up of queues by forcing faster arrivals and reducing the number of resources.

g.arrival_df = "ed_arrivals_more_frequent.csv"
g.n_cubicles_trauma_treat = 3
g.n_cubicles_non_trauma_treat = 2
g.n_exam = 2

my_trial = Trial()

my_trial.run_trial()

animate_activity_log(
    event_log=my_trial.trial_logger,
    event_position_df=event_position_df,
    run_number=1,
    scenario=g(),
    debug_mode=True,
    setup_mode=False,
    every_x_time_units=5,
    include_play_button=True,
    gap_between_entities=11,
    gap_between_resources=15,
    gap_between_resource_rows=30,
    gap_between_queue_rows=30,
    plotly_height=600,
    plotly_width=1000,
    override_x_max=700,
    override_y_max=675,
    entity_icon_size=10,
    resource_icon_size=13,
    text_size=15,
    wrap_queues_at=10,
    step_snapshot_max=20,
    limit_duration=g.sim_duration,
    time_display_units="dhm",
    display_stage_labels=False,
    add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_2_branching_multistep/Full%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
    step_snapshot_limit_gauges=True,
)

Animation function called at 13:53:28
Iteration through time-unit-by-time-unit logs complete 13:53:28
Snapshot df concatenation complete at 13:53:28
Reshaped animation dataframe finished construction at 13:53:29
Placement dataframe started construction at 13:53:29
Placement dataframe finished construction at 13:53:29
Output animation generation complete at 13:53:30
Total Time Elapsed: 2.42 seconds