https://github.com/galenseilis/DESimpy

Discrete event simulation in synchronous Python (DESimpy).
https://github.com/galenseilis/DESimpy

Last synced: 5 days ago
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Discrete event simulation in synchronous Python (DESimpy).

• Host: GitHub
• URL: https://github.com/galenseilis/DESimpy
• Owner: galenseilis
• License: apache-2.0
• Created: 2024-01-15T01:23:57.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-11-09T06:02:22.000Z (5 days ago)
• Last Synced: 2024-11-09T22:17:30.021Z (5 days ago)
• Language: Python
• Homepage:
• Size: 2.31 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-des - Github

README

        
# DESimpy

A synchronous [discrete event simulation](https://en.wikipedia.org/wiki/Discrete-event_simulation) (DES) framework in Python (DESimpy).

## Overview

DESimPy provides the core components of DES.

Processes in DESimPy are defined by methods owned by Python objects inherited from the `Event` abstract base class. These processes can be used to model system-level or component level changes in a modelled system. Such systems might include customers or patients flowing through services, vehicles in traffic, or agents competing in games.

DESimPy implements time-to-event simulation where the next event in a schedule is processed next regardless of the amount of time in the simulated present to that event. This constrasts with "time sweeping" in which a step size is used to increment foreward in time. It is possible to combine time-to-event with time sweeping (see [Palmer & Tian 2021](https://www.semanticscholar.org/paper/Implementing-hybrid-simulations-that-integrate-in-Palmer-Tian/bea73e8d6c828e15290bc4f01c8dd1a4347c46d0)), however this package does not provide any explicit support for that.

## Installation

```bash

pip install desimpy

```

## Quickstart

Here is a small example to show the basic logic. This example is the simple clock process presented in the [SimPy documentation](https://simpy.readthedocs.io/en/stable/index.html).

```python

from desimpy import EventScheduler

def clock(env: EventScheduler, name: str, tick: int | float) -> None:

    """Clock simulation process."""

    def action() -> None:

        """Schedule next tick of the clock."""

        print(name, env.current_time)

        env.timeout(tick, action)

    env.timeout(0, action=action)

env = EventScheduler()

clock(env, "fast", 0.5)

clock(env, "slow", 1)

event_log = env.run_until_max_time(2)

```

# Design

- Avoid performance overhead of coroutines.

- Do not change the past (i.e. event log should not be changed during simulation).

- Event schedule ordering is purely temporal order of events elapsing.

- Built-in event logging.

    - Can be turned off for better performance.

- Future schedule is mutable to simulate interruptions.

    - Activating events.

    - Deactivating events.

    - Cancelling events.