Use of asyncio event loop recursively - python-3.x

Question

I am new to asyncio ( used with python3.4 ) and I am not sure I use it as one should. I have seen in this thread that it can be use to execute a function every n seconds (in my case ms) without having to dive into threading.
I use it to get data from laser sensors through a basic serial protocol every n ms until I get m samples.
Here is the definition of my functions :
def countDown( self,
loop,
funcToDo,
*args,
counter = [ 1 ],
**kwargs ):
""" At every call, it executes funcToDo ( pass it args and kwargs )
and count down from counter to 0. Then, it stop loop """
if counter[ 0 ] == 0:
loop.stop()
else:
funcToDo( *args, **kwargs )
counter[ 0 ] -= 1
def _frangeGen( self, start = 0, stop = None, step = 1 ):
""" use to generate a time frange from start to stop by step step """
while stop is None or start < stop:
yield start
start += step
def callEvery( self,
loop,
interval,
funcToCall,
*args,
now = True,
**kwargs ):
""" repeat funcToCall every interval sec in loop object """
nb = kwargs.get( 'counter', [ 1000 ] )
def repeat( now = True,
times = self._frangeGen( start = loop.time(),
stop=loop.time()+nb[0]*interval,
step = interval ) ):
if now:
funcToCall( *args, **kwargs )
loop.call_at( next( times ), repeat )
repeat( now = now )
And this is how I use it (getAllData is the function that manage serial communication) :
ts = 0.01
nbOfSamples = 1000
loop = asyncio.get_event_loop()
callEvery( loop, ts, countDown, loop, getAllData, counter = [nbOfSamples] )
loop.run_forever()
I want to put that bloc into a function and call it as often as I want, something like this :
for i in range( nbOfMeasures ):
myFunction()
processData()
But the second test does not call getAllData 1000 times, only twice, sometimes thrice. The interesting fact is one time in two I get as much data as I want. I don't really understand, and I can't find anything in the docs, so I am asking for your help. Any explanation or an easier way to do it is gladly welcome :)

Answer 1

You are complicating things too much and, generally speaking, doing recursion when you have an event loop is bad design.
asyncio is fun only when you make use of coroutines. Here's one way of doing it:
import asyncio as aio
def get_laser_data():
"""
get data from the laser using blocking IO
"""
...
#aio.coroutine
def get_samples(loop, m, n):
"""
loop = asyncio event loop
m = number of samples
n = time between samples
"""
samples = []
while len(samples) < m:
sample = yield from loop.run_in_executor(None, get_laser_data)
samples.append(sample)
yield from aio.sleep(n)
return samples
#aio.coroutine
def main(loop):
for i in range(nbOfMeasures):
samples = yield from get_samples(loop, 1000, 0.01)
...
loop = aio.get_event_loop()
loop.run_until_complete(main(loop))
loop.close()
If you are completely confused by this, consider reading some tutorials/documentation about asyncio.
But I would like to point out that you must use a thread to get the data from the laser sensor. Doing any blocking IO in the same thread that the event loop is running will block the loop and throw off aio.sleep. This is what yield from loop.run_in_executor(None, get_laser_data) is doing. It's running the get_laser_data function in a separate thread.

Answer 2

In python 3.5, you can make use of the async for syntax and create an asynchronous iterator to control your time frames. It has to implement the __aiter__ and __anext__ methods:
class timeframes(collections.AsyncIterator):
def __init__(self, steps, delay=1.0, *, loop=None):
self.loop = asyncio.get_event_loop() if loop is None else loop
self.ref = self.loop.time()
self.delay = delay
self.steps = steps
self.iter = iter(range(steps))
async def __anext__(self):
try:
when = self.ref + next(self.iter) * self.delay
except StopIteration:
raise StopAsyncIteration
else:
future = asyncio.Future()
self.loop.call_at(when, future.set_result, None)
await future
return self.loop.time()
async def __aiter__(self):
return self
Here's a coroutine that simulates an execution:
async def simulate(steps, delay, execution):
# Prepare timing
start = loop.time()
expected = steps * delay - delay + execution
# Run simulation
async for t in timeframes(steps, delay):
await loop.run_in_executor(None, time.sleep, execution)
# Return error
result = loop.time() - start
return result - expected
And this is the kind of result you'll get on a linux OS:
>>> loop = asyncio.get_event_loop()
>>> simulation = simulate(steps=1000, delay=0.020, execution=0.014)
>>> error = loop.run_until_complete(simulation)
>>> print("Overall error = {:.3f} ms".format(error * 1000))
Overall error = 1.199 ms
It is different on a windows OS (see this answer) but the event loop will catch up and the overall error should never exceed 15 ms.