Usage

In this section, I’ll explain how to wrap a callback-style API in an async/await-style API, using sched as an example. Let’s start by writing code that does the following, and remember how to use the module:

  1. Waits for 1 second

  2. Prints “A”

  3. Waits for 2 seconds

  4. Prints “B”

  5. Waits for 3 seconds

  6. Prints “C”

Our code would look like this:

import sched

PRIORITY = 0
s = sched.scheduler()

def task():
    def step1():
        print('A')
        s.enter(2, PRIORITY, step2)

    def step2():
        print('B')
        s.enter(3, PRIORITY, step3)

    def step3():
        print('C')

    s.enter(1, PRIORITY, step1)

task()
s.run()

sched provides callback-style APIs, and the code that uses them is not easy to understand. You might wonder, “Why not just use time.sleep()?” So let me address that first.

Counter to ‘Why not just use time.sleep?’

Indeed, if you use time.sleep, it’ll be easier to understand.

from time import sleep

def task():
    sleep(1)
    print('A')
    sleep(2)
    print('B')
    sleep(3)
    print('C')

task()

However, this approach works only when there’s a single task. If there are multiple ones, the story changes. With the time.sleep method, you need multiple threads to run multiple tasks simultaneously, whereas with the sched method, one thread is sufficient. For example, you can just call the task() multiple times at the end of the code:

task()
task()
s.run()

Therefore, sched cannot be replaced with time.sleep.

API Design

We want to wrap the API, but first, we need to imagine how we want to use it from asyncgui. Remember the time.sleep example we saw earlier:

from time import sleep

def task():
    sleep(1)
    print('A')
    sleep(2)
    print('B')
    sleep(3)
    print('C')

This approach has the disadvantage of occupying a thread, but in return, it’s quite easy to read. If we successfully design our API to be as readable as this, we’d have the best of both worlds [1]. So, let’s aim for it.

# our ideal
async def task():
    await sleep(1)
    print('A')
    await sleep(2)
    print('B')
    await sleep(3)
    print('C')

Using it like await sleep(1) means the sleep must be a collections.abc.Callable that returns an collections.abc.Awaitable. There are several options that meet this condition, and we choose an async function [2].

async def sleep(duration):
    ...

But hold on, since sched.scheduler.enter() is an instance method, our API needs to take a sched.scheduler instance. And since it has the priority parameter, our API might better have one as well in order not to lose any functionality of the original API.

async def sleep(scheduler, priority, duration):
    ...

Let’s start implementing it with this goal in mind.

Implementation

To wrap a callback-style API in an async/await-style API, we need to set up execution to resume when a callback function is called, and then pause it. This might sound unclear, but if you’ve ever used asyncio.Event or trio.Event, you already know it.

import asyncio

async def wrapper():
    e = asyncio.Event()

    # Set up the execution to resume when a callback function is called
    register_callback(lambda *args, **kwargs: e.set())

    # Pause the execution
    await e.wait()

async def user():
    print('A')
    await wrapper()
    print('B')

By introducing a wrapper like this, the user side code can use a callback-style API without losing readability. And asyncgui has an API specifically designed for this purpose.

import asyncgui as ag

async def wrapper():
    e = ag.ExclusiveEvent()
    register_callback(e.fire)  # A
    args, kwargs = await e.wait()  # B

asyncgui.ExclusiveEvent has two advantages over asyncio.Event. One, you don’t need to use a lambda because asyncgui.ExclusiveEvent.fire() can take any arguments (line A). Two, you can receive the arguments passed to fire (line B).

Let’s implement our API with this.

import asyncgui as ag

async def sleep(scheduler, priority, duration):
    e = ag.ExclusiveEvent()
    scheduler.enter(duration, priority, e.fire)
    await e.wait()

Now we can use it like this:

import functools
import sched
import asyncgui as ag

async def sleep(...):
    ...

def main():
    s = sched.scheduler()
    slp = functools.partial(sleep, s, 0)
    ag.start(task(slp))
    s.run()

async def task(slp):
    await slp(1)
    print('A')
    await slp(2)
    print('B')
    await slp(3)
    print('C')

main()

We successfully achieved the best of both worlds; our API doesn’t occupy a thread, and the user side code is as readable as the time.sleep() example.

However, there’s one more thing to address: Dealing with Cancellation. It is not strictly necessary in this case because ExclusiveEvent handles it to a certain extent, but it’s better to handle it within sleep itself to cover some edge cases.

import asyncgui as ag

async def sleep(scheduler, priority, duration):
    e = ag.ExclusiveEvent()
    event = scheduler.enter(duration, priority, e.fire)
    try:
        await e.wait()
    except ag.Cancelled:
        scheduler.cancel(event)
        raise

This is the complete version of our API. We successfully connected the sched module to the asyncgui module. Once connected, we can benefit from the powerful Structured Concurrency APIs.

import functools
import sched
import asyncgui as ag
import string

async def sleep(scheduler, priority, duration):
    ...

def main():
    s = sched.scheduler()
    slp = functools.partial(sleep, s, 0)
    ag.start(async_main(slp))
    s.run()

async def async_main(slp):
    # Print digits from 0 to 9 at 0.3-second intervals, with a 2-second time limit
    async with ag.move_on_when(slp(2)) as timeout_tracker:
        for c in string.digits:
            print(c, end=' ')
            await slp(0.3)
    print('')

    if timeout_tracker.finished:
        print("Timeout")
    else:
        print("Printed all digits in time")

main()

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Timeout