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nesting in a ParallelActivity

nesting in a ParallelActivity#

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

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= 3,
)
a2 = model.BasicActivity(
    env= my_env,
    name= "a2",
    ID= "a2",
    registry= registry,
    duration= 2,
)
a3 = model.BasicActivity(
    env= my_env,
    name= "a3",
    ID= "a3",
    registry= registry,
    duration= 5,
)

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

Pa=model.ParallelActivity(    
    env= my_env,
    name= "Pa",
    ID= "Pa",
    registry= registry,
    sub_processes= [a, a2, a3],
)

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

b = model.BasicActivity(
    ID= "b",
    env= my_env,
    name= "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#

pd.DataFrame(Pa.log)
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:01 Pa START {} {}
1 1970-01-01 00:00:01 Pa START {} {'type': 'subprocess', 'ref': 'a'}
2 1970-01-01 00:00:01 Pa START {} {'type': 'subprocess', 'ref': 'a2'}
3 1970-01-01 00:00:01 Pa START {} {'type': 'subprocess', 'ref': 'a3'}
4 1970-01-01 00:00:03 Pa STOP {} {'type': 'subprocess', 'ref': 'a2'}
5 1970-01-01 00:00:04 Pa STOP {} {'type': 'subprocess', 'ref': 'a'}
6 1970-01-01 00:00:06 Pa STOP {} {'type': 'subprocess', 'ref': 'a3'}
7 1970-01-01 00:00:06 Pa STOP {} {} 
pd.DataFrame(Sc.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': 'Pa'}
4 1970-01-01 00:00:06 Sc STOP {} {'type': 'subprocess', 'ref': 'Pa'}
5 1970-01-01 00:00:06 Sc STOP {} {} 
pd.DataFrame(c.log)
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 c START {} {}
1 1970-01-01 00:00:01 c STOP {} {} 
pd.DataFrame(Sb.log)
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 Sb START {} {}
1 1970-01-01 00:00:00 Sb START {} {'type': 'subprocess', 'ref': 'b'}
2 1970-01-01 00:00:14 Sb STOP {} {'type': 'subprocess', 'ref': 'b'}
3 1970-01-01 00:00:14 Sb STOP {} {} 
pd.DataFrame(b.log)
Timestamp ActivityID ActivityState ObjectState ActivityLabel
0 1970-01-01 00:00:00 b WAIT_START {} {}
1 1970-01-01 00:00:04 b WAIT_STOP {} {}
2 1970-01-01 00:00:04 b START {} {}
3 1970-01-01 00:00:14 b STOP {} {}

5.2 Visualise gantt charts#

plot.get_gantt_chart([Sc,c,Pa,a,a2,a3,b])
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