SoyutNet

SoyutNet is a place/transition net (PT net, Petri net) simulator that uses Python's asyncio task and synchronization utilities as backend. (Soyut means abstract in Turkish.)

Its documentation can be found at https://soyutnet.readthedocs.io

Building

python3 -m venv venv
source venv/bin/activate
pip install -e '.[dev]'
pytest

Installing

python3 -m venv venv
source venv/bin/activate
pip install soyutnet

An example

This example simulates the PT net given in the diagram below.

import sys
import asyncio

import soyutnet
from soyutnet import SoyutNet
from soyutnet.constants import GENERIC_ID, GENERIC_LABEL


def main():
    async def scheduled():
        await asyncio.sleep(1)
        soyutnet.terminate()

    net = SoyutNet()
    net.DEBUG_ENABLED = True

    LABEL = 1
    initial_tokens = {
        GENERIC_LABEL: [GENERIC_ID],
        LABEL: [1000, 990],
    }
    reg = net.PTRegistry()
    o1 = net.Observer(verbose=True)
    p1 = net.Place("p1", initial_tokens=initial_tokens, observer=o1)
    o2 = net.Observer(verbose=True)
    p2 = net.Place("p2", observer=o2)
    t1 = net.Transition("t1")
    """Define places and transitions (PTs)"""

    p1.connect(t1, labels=[GENERIC_LABEL, LABEL]).connect(
        p2, labels=[GENERIC_LABEL, LABEL]
    )
    """Connect PTs"""

    reg.register(p1)
    reg.register(p2)
    reg.register(t1)
    """Save to a list of PTs"""

    soyutnet.run(reg, extra_routines=[scheduled()])
    print("Simulation is terminated.")

    records = reg.get_merged_records()
    graph = reg.generate_graph(indent="  ", label_names={ LABEL: "🤔", GENERIC_LABEL: "🤌" })

    print("\nRecorded events:")
    {None: net.print(rec) for rec in records}
    print("\nNet graph:")
    print(graph, file=sys.stderr)

    return records, graph


if __name__ == "__main__":
    main()

outputs:

$ python tests/readme_example.py

loop(t1, 3): REC: O{(p1, 1)}: (193215.922177, ((0, 1, ), (1, 2, ), ), t1, ) 
loop(t1, 3): REC: O{(p1, 1)}: (193215.922371, ((0, 0, ), (1, 2, ), ), t1, ) 
loop(t1, 3): REC: O{(p1, 1)}: (193215.922544, ((0, 0, ), (1, 1, ), ), t1, ) 
Simulation is terminated.

Recorded events:
(p1, (193215.922177, ((0, 1, ), (1, 2, ), ), t1, ), ) 
(p1, (193215.922371, ((0, 0, ), (1, 2, ), ), t1, ), ) 
(p1, (193215.922544, ((0, 0, ), (1, 1, ), ), t1, ), ) 

Net graph:
digraph Net {
  subgraph cluster_0 {
    p1_0 [shape="circle",fontsize="20",style="filled",color="#000000",fillcolor="#dddddd",label="",xlabel="p1",height="1",width="1",penwidth=3];
    p2_0 [shape="circle",fontsize="20",style="filled",color="#000000",fillcolor="#dddddd",label="",xlabel="p2",height="1",width="1",penwidth=3];
    t1_0 [shape="box",fontsize="20",style="filled",color="#cccccc",fillcolor="#000000",label="",xlabel="t1",height="0.25",width="1.25",penwidth=3];
    t1_0 -> p2_0 [fontsize="20",label="{🤌,🤔}",minlen="2",penwidth="3"];
    p1_0 -> t1_0 [fontsize="20",label="{🤌,🤔}",minlen="2",penwidth="3"];
  }
  clusterrank=none;
}

How to interpret events

('p1', (188597.931257369, ((0, 1), (1, 2)), 't1'))

A list of place markings that show token counts for each label recorded just before a transition is fired:
[('<name of the place>', (<event timestamp in seconds>, ((<token label>, <token count>),), '<firing transition>')), ...]

How to generate the graph

sudo apt install graphviz # Which provides 'dot'
python tests/readme_example.py 2>&1 > /dev/null | dot -Tpng > readme_example.png

Outputs:

Credits