lpsim: A Clear and Visible Python Library!

The Python lpsim module is a lightweight simulation tool, adept at event flow and resource scheduling simulations.

It can be used to simulate device workflows and simple business scenarios, making it suitable for quickly validating logic.

Reading this article will take about 5 minutes, and by following the practical examples, you can easily grasp the core usage of lpsim.

1. Simulating Simple Event Flows

When validating processes, it is often necessary to simulate events that are triggered in sequence. The Event class in lpsim can easily achieve this.

The following code simulates the event flow of “Device Start → Data Collection”.

It clearly shows the order of event triggers, and the code is concise and easy to understand:

from lpsim import Event, Simulator

def start_device():
    print("Device started successfully")

def collect_data():
    print("Data collection completed")

# Create simulator and events
sim = Simulator()
sim.add_event(Event(0, start_device))  # Trigger start at 0 seconds
sim.add_event(Event(2, collect_data))  # Trigger collection at 2 seconds
sim.run()  # Output: Device started successfully → Data collection completed

This method is particularly suitable for testing simple timing logic without writing complex scheduling code.

2. Resource Allocation Simulation

In multitasking scenarios, resource allocation is a common issue.

The Resource class in lpsim can simulate the process of resource occupation and release.

The following code simulates a scenario where 2 tasks compete for 1 resource;

It intuitively displays the results of resource allocation:

from lpsim import Resource, Simulator, Process

def task(name, res, sim):
    with res.request():
        print(f"{name} occupies the resource")
        yield sim.timeout(3)  # Occupies for 3 seconds
        print(f"{name} releases the resource")

# Initialize
sim = Simulator()
res = Resource(sim, capacity=1)  # 1 resource

# Add 2 tasks
sim.add_process(Process(sim, task, "Task 1", res, sim))
sim.add_process(Process(sim, task, "Task 2", res, sim))
sim.run()  # Output: Task 1 occupies → Task 1 releases → Task 2 occupies → Task 2 releases

This example helps quickly understand the core logic of resource scheduling without manually handling lock mechanisms.

3. Timeline Visualization

Simulation results need to be visually displayed over time…

The Timeline class in lpsim can generate a simple timeline, helping us clarify the order of events.

The following code adds timeline output to the event flow, making the simulation process clearer and easier to debug:

from lpsim import Event, Simulator, Timeline

def log_msg(msg):
    print(msg)

# Create components
sim = Simulator()
timeline = Timeline(sim)
sim.add_event(Event(1, log_msg, "User logged in"))
sim.add_event(Event(3, log_msg, "Order submitted"))

# Run and output timeline
sim.run()
timeline.show()  # Output: 1s: User logged in → 3s: Order submitted

With the timeline, even with many events, it is easy to quickly locate the occurrence time of each event, making it convenient to troubleshoot timing issues.

4. Comparison of Advantages and Disadvantages of lpsim

Compared to the complex simPy module, lpsim is smaller and has a lower learning curve.

There is no need to master complex concepts, allowing beginners to quickly get started.

However, its functionality is relatively basic, and it lacks support for complex scenarios (such as nested scheduling of multiple resources), which may not be sufficient for large simulation projects.

It is recommended to use lpsim for small simulations and rapid prototyping, while for complex projects, simPy should be prioritized.

5. Summary and Interaction

This article discussed three practical cases of the lpsim module and its advantages and disadvantages, helping everyone improve simulation efficiency in suitable scenarios.

What interesting simulations have you done with lpsim?

Or do you have other useful simulation modules to recommend? Feel free to share in the comments!

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