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.

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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:

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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.

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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:

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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.

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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:

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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.

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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:

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(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.

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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:

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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.

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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:

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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.

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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:

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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.

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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:

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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.

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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:

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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.

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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:

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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.