144. Scheduling Tasks with sched

The sched module in Python provides a way to schedule tasks to run at specific times. It is an event-driven scheduling library, where tasks can be scheduled to run after a certain delay, or at a certain time, and the scheduler will manage the timing for task execution.

Here are 10 Python code snippets demonstrating how to schedule tasks using the sched module:

1. Basic Task Scheduling

Scheduling a simple task to run after a delay.

import sched
import time

# Create a scheduler instance
scheduler = sched.scheduler(time.time, time.sleep)

# Define a simple task
def my_task():
    print("Task executed!")

# Schedule the task to run after 2 seconds
scheduler.enter(2, 1, my_task)

# Run the scheduler
scheduler.run()

Output:

Task executed!

Explanation:

• enter(delay, priority, action) schedules my_task() to run after 2 seconds.

• scheduler.run() processes the scheduled events.

2. Scheduling Multiple Tasks

Scheduling multiple tasks at different times.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

# Define tasks
def task_one():
    print("Task One executed!")

def task_two():
    print("Task Two executed!")

# Schedule tasks with different delays
scheduler.enter(1, 1, task_one)
scheduler.enter(3, 1, task_two)

# Run the scheduler
scheduler.run()

Output:

Task One executed!
Task Two executed!

Explanation:

• Two tasks are scheduled to run at 1 second and 3 seconds respectively.

3. Scheduling Tasks Repeatedly

Scheduling a task to repeat at fixed intervals.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def repeat_task():
    print("Repeated Task executed!")
    # Schedule the task again after 3 seconds
    scheduler.enter(3, 1, repeat_task)

# Initial task scheduling
scheduler.enter(3, 1, repeat_task)

# Run the scheduler
scheduler.run()

Output:

Repeated Task executed!
Repeated Task executed!
Repeated Task executed!
...

Explanation:

• The task repeats every 3 seconds by re-scheduling itself each time it runs.

4. Canceling a Scheduled Task

Canceling a task before it runs.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_to_cancel():
    print("This task will not be executed.")

# Schedule the task
event = scheduler.enter(5, 1, task_to_cancel)

# Cancel the task before it runs
scheduler.cancel(event)

# Run the scheduler
scheduler.run()

Output:

(no output)

Explanation:

• The task is scheduled, but canceled before it has a chance to run.

5. Task Scheduling with Priority

Scheduling tasks with different priorities.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_high_priority():
    print("High priority task executed!")

def task_low_priority():
    print("Low priority task executed!")

# Schedule tasks with different priorities
scheduler.enter(2, 2, task_low_priority)  # Lower priority
scheduler.enter(1, 1, task_high_priority)  # Higher priority

# Run the scheduler
scheduler.run()

Output:

High priority task executed!
Low priority task executed!

Explanation:

• Tasks with lower priority values run first. In this case, the high priority task runs before the low priority task.

6. Task Scheduling with Absolute Time

Scheduling a task to run at an absolute time.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_at_specific_time():
    print("Task executed at the specific time!")

# Schedule the task to run at an absolute time (current time + 5 seconds)
run_time = time.time() + 5
scheduler.enterabs(run_time, 1, task_at_specific_time)

# Run the scheduler
scheduler.run()

Output:

Task executed at the specific time!

Explanation:

• enterabs(time, priority, action) schedules the task to run at an exact time (absolute time).

7. Multiple Scheduled Tasks with Different Delays

Scheduling multiple tasks with different delays and priorities.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_1():
    print("Task 1 executed.")

def task_2():
    print("Task 2 executed.")

# Schedule tasks with different delays and priorities
scheduler.enter(1, 1, task_1)  # Execute after 1 second
scheduler.enter(2, 2, task_2)  # Execute after 2 seconds

# Run the scheduler
scheduler.run()

Output:

Task 1 executed.
Task 2 executed.

Explanation:

• Two tasks are scheduled to run after 1 and 2 seconds respectively.

8. Handling Task Arguments

Passing arguments to the scheduled tasks.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_with_args(arg1, arg2):
    print(f"Task executed with arguments: {arg1}, {arg2}")

# Schedule the task with arguments
scheduler.enter(2, 1, task_with_args, argument=("Hello", "World"))

# Run the scheduler
scheduler.run()

Output:

Task executed with arguments: Hello, World

Explanation:

• Arguments can be passed to tasks using the argument parameter in enter().

9. Using Delay Time Dynamically

Scheduling tasks with dynamically calculated delay times.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task_dynamic_delay():
    print("Task executed with dynamic delay.")

# Schedule the task after a dynamically calculated delay
delay_time = 5 * 2  # 10 seconds delay
scheduler.enter(delay_time, 1, task_dynamic_delay)

# Run the scheduler
scheduler.run()

Output:

Task executed with dynamic delay.

Explanation:

• The delay time is calculated dynamically and passed to the enter() method.

10. Scheduling Tasks from a List

Scheduling tasks from a list of functions and delays.

import sched
import time

scheduler = sched.scheduler(time.time, time.sleep)

def task1():
    print("Task 1 executed.")

def task2():
    print("Task 2 executed.")

tasks = [
    (1, task1),
    (2, task2),
]

# Loop through the task list and schedule each one
for delay, task in tasks:
    scheduler.enter(delay, 1, task)

# Run the scheduler
scheduler.run()

Output:

Task 1 executed.
Task 2 executed.

Explanation:

• A list of tasks is used to schedule them at different times.

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Conclusion:

The sched module is a powerful tool for scheduling tasks and events in Python, particularly useful in event-driven applications. These examples demonstrate how to schedule single or recurring tasks, manage priorities, and pass arguments to tasks.