Simpy

# SimPy Events System

This guide covers the event system in SimPy, which forms the foundation of discrete-event simulation.

## Event Basics

Events are the core mechanism for controlling simulation flow. Processes yield events and resume when those events are triggered.

### Event Lifecycle

Events progress through three states:

1. **Not triggered** - Initial state as memory objects
2. **Triggered** - Scheduled in event queue; `triggered` property is `True`
3. **Processed** - Removed from queue with callbacks executed; `processed` property is `True`

```python
import simpy

env = simpy.Environment()

# Create an event
event = env.event()
print(f'Triggered: {event.triggered}, Processed: {event.processed}')  # Both False

# Trigger the event
event.succeed(value='Event result')
print(f'Triggered: {event.triggered}, Processed: {event.processed}')  # True, False

# Run to process the event
env.run()
print(f'Triggered: {event.triggered}, Processed: {event.processed}')  # True, True
print(f'Value: {event.value}')  # 'Event result'
```

## Core Event Types

### Timeout

Controls time progression in simulations. Most common event type.

```python
import simpy

def process(env):
    print(f'Starting at {env.now}')
    yield env.timeout(5)
    print(f'Resumed at {env.now}')

    # Timeout with value
    result = yield env.timeout(3, value='Done')
    print(f'Result: {result} at {env.now}')

env = simpy.Environment()
env.process(process(env))
env.run()
```

**Usage:**
- `env.timeout(delay)` - Wait for specified time
- `env.timeout(delay, value=val)` - Wait and return value

### Process Events

Processes themselves are events, allowing processes to wait for other processes to complete.

```python
import simpy

def worker(env, name, duration):
    print(f'{name} starting at {env.now}')
    yield env.timeout(duration)
    print(f'{name} finished at {env.now}')
    return f'{name} result'

def coordinator(env):
    # Start worker processes
    worker1 = env.process(worker(env, 'Worker 1', 5))
    worker2 = env.process(worker(env, 'Worker 2', 3))

    # Wait for worker1 to complete
    result = yield worker1
    print(f'Coordinator received: {result}')

    # Wait for worker2
    result = yield worker2
    print(f'Coordinator received: {result}')

env = simpy.Environment()
env.process(coordinator(env))
env.run()
```

### Event

Generic event that can be manually triggered.

```python
import simpy

def waiter(env, event):
    print(f'Waiting for event at {env.now}')
    value = yield event
    print(f'Event received with value: {value} at {env.now}')

def triggerer(env, event):
    yield env.timeout(5)
    print(f'Triggering event at {env.now}')
    event.succeed(value='Hello!')

env = simpy.Environment()
event = env.event()
env.process(waiter(env, event))
env.process(triggerer(env, event))
env.run()
```

## Composite Events

### AllOf - Wait for Multiple Events

Triggers when all specified events have occurred.

```python
import simpy

def process(env):
    # Start multiple tasks
    task1 = env.timeout(3, value='Task 1 done')
    task2 = env.timeout(5, value='Task 2 done')
    task3 = env.timeout(4, value='Task 3 done')

    # Wait for all to complete
    results = yield simpy.AllOf(env, [task1, task2, task3])
    print(f'All tasks completed at {env.now}')
    print(f'Results: {results}')

    # Alternative syntax using & operator
    task4 = env.timeout(2)
    task5 = env.timeout(3)
    yield task4 & task5
    print(f'Tasks 4 and 5 completed at {env.now}')

env = simpy.Environment()
env.process(process(env))
env.run()
```

**Returns:** Dictionary mapping events to their values

**Use cases:**
- Parallel task completion
- Barrier synchronization
- Waiting for multiple resources

### AnyOf - Wait for Any Event

Triggers when at least one specified event has occurred.

```python
import simpy

def process(env):
    # Start multiple tasks with different durations
    fast_task = env.timeout(2, value='Fast')
    slow_task = env.timeout(10, valu