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nesting in a SequentialActivity

nesting in a SequentialActivity#

This notebook provides an example of OpenCLSim’s capability to nest various processes in a SequentialActivity.

0. Import libraries#

import datetime, time
import simpy

import pandas as pd

import openclsim.core as core
import openclsim.model as model
import openclsim.plot as plot

1. Initialise simpy environment#

# setup environment (simulation time needs to match the available weather data)
simulation_start = 0
my_env = simpy.Environment(initial_time=simulation_start)

2. Define object classes#

In this example we won’t use object classes

3. Create objects#

3.1. Create site object(s)#

No site objects are created

3.2. Create vessel object(s)#

No vessel objects are created

3.3 Create activity/activities#

# initialise registry
registry = {}

a = model.BasicActivity(
    env= my_env,
    name= "a",
    ID= "a",
    registry= registry,
    duration= 1,
)
a2 = model.BasicActivity(
    env= my_env,
    name= "a2",
    ID= "a2",
    registry= registry,
    duration= 1,
)

Sa=model.SequentialActivity(    
    env= my_env,
    name= "Sa",
    ID= "Sa",
    registry= registry,
    sub_processes= [a, a2],
)

c = model.BasicActivity(
    env= my_env,
    name= "c",
    ID= "c",
    registry= registry,
    duration= 1,
)

Sc=model.SequentialActivity(    
    env= my_env,
    name= "Sc",
    ID= "Sc",
    registry= registry,
    sub_processes= [c, Sa],
)

b = model.BasicActivity(
    env= my_env,
    name= "b",
    ID= "b",
    registry= registry,
    duration= 10,
    start_event=[
        {
            "name": "a",
            "type": "activity",
            "state": "done"
        }
    ]

)

Sb=model.SequentialActivity(    
    env= my_env,
    name= "Sb",
    ID= "Sb",
    registry= registry,
    sub_processes= [b],
)

4. Register processes and run simpy#

model.register_processes([Sc,Sb])
my_env.run()

5. Inspect results#

5.1 Inspect logs#

display(pd.DataFrame(Sc.log))

display(pd.DataFrame(c.log))
display(pd.DataFrame(Sa.log))

display(pd.DataFrame(a.log))
display(pd.DataFrame(a2.log))

display(pd.DataFrame(b.log))
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 Sc START {} {}
1 1970-01-01 00:00:00 Sc START {} {'type': 'subprocess', 'ref': 'c'}
2 1970-01-01 00:00:01 Sc STOP {} {'type': 'subprocess', 'ref': 'c'}
3 1970-01-01 00:00:01 Sc START {} {'type': 'subprocess', 'ref': 'Sa'}
4 1970-01-01 00:00:03 Sc STOP {} {'type': 'subprocess', 'ref': 'Sa'}
5 1970-01-01 00:00:03 Sc STOP {} {}
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 c START {} {}
1 1970-01-01 00:00:01 c STOP {} {}
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:01 Sa START {} {}
1 1970-01-01 00:00:01 Sa START {} {'type': 'subprocess', 'ref': 'a'}
2 1970-01-01 00:00:02 Sa STOP {} {'type': 'subprocess', 'ref': 'a'}
3 1970-01-01 00:00:02 Sa START {} {'type': 'subprocess', 'ref': 'a2'}
4 1970-01-01 00:00:03 Sa STOP {} {'type': 'subprocess', 'ref': 'a2'}
5 1970-01-01 00:00:03 Sa STOP {} {}
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:01 a START {} {}
1 1970-01-01 00:00:02 a STOP {} {}
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:02 a2 START {} {}
1 1970-01-01 00:00:03 a2 STOP {} {}
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 b WAIT_START {} {}
1 1970-01-01 00:00:02 b WAIT_STOP {} {}
2 1970-01-01 00:00:02 b START {} {}
3 1970-01-01 00:00:12 b STOP {} {}

5.2 Visualise gantt charts#

plot.get_gantt_chart([Sc,c,Sa,a,a2,b])
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