Postpone code for later execution in python (like setTimeout in javascript)
Question:
I have to do a program in python that needs to execute for some time and then (does not matter where it was executing) it must dump information to a file, close the file and then exit.
The behavior here is equivalent in JavaScript to using setTimeout(func, 1000000) where its first parameter (func) would be a pointer to the function with the exit code and its second parameter would be the time available to the program to execute.
I know how to make this program in C (using SO signals) but with python
Answers:
You can use signals in python as well (unix only)
import signal, sys
# install a SIGALRM handler
def handler(signum, frame):
print "got signal, exiting"
sys.exit(1)
signal.signal(signal.SIGALRM, handler)
# emit SIGALRM after 5 secs
signal.setitimer(signal.ITIMER_REAL, 5)
# do stuff
i = 1
while True:
if i % 100000 == 0:
print i
i += 1
In practice, a Timer is probably the simplest way to do what you want.
This code will do the following:
- After 1 second, it prints “arg1 arg2”
- After 2 seconds, it prints “OWLS OWLS OWLS”
===
from threading import Timer
def twoArgs(arg1,arg2):
print arg1
print arg2
print ""
def nArgs(*args):
for each in args:
print each
#arguments:
#how long to wait (in seconds),
#what function to call,
#what gets passed in
r = Timer(1.0, twoArgs, ("arg1","arg2"))
s = Timer(2.0, nArgs, ("OWLS","OWLS","OWLS"))
r.start()
s.start()
===
The above code will most likely solve your problem.
But! There is alternative way, that doesn’t use multithreading. It works much more like Javascript, which is single-threaded.
For this single-thread version, all you need to do is store the function and its arguments in an object, along with the time at which the function should be run.
Once you have the object containing the function call and the timeout, just periodically check if the function is ready to execute.
The right way to do this is by making a priority queue to store all of the functions we want to run in the future, as shown in the code below.
Just like in Javascript, this approach makes no guarantee that the function will be run exactly on time. A function that takes a very long time to run will delay the functions after it. But it does guarantee that a function will be run no sooner than its timeout.
This code will do the following:
- After 1 second, it prints “20”
- After 2 seconds, it prints “132”
- After 3 seconds, it quits.
===
from datetime import datetime, timedelta
import heapq
# just holds a function, its arguments, and when we want it to execute.
class TimeoutFunction:
def __init__(self, function, timeout, *args):
self.function = function
self.args = args
self.startTime = datetime.now() + timedelta(0,0,0,timeout)
def execute(self):
self.function(*self.args)
# A "todo" list for all the TimeoutFunctions we want to execute in the future
# They are sorted in the order they should be executed, thanks to heapq
class TodoList:
def __init__(self):
self.todo = []
def addToList(self, tFunction):
heapq.heappush(self.todo, (tFunction.startTime, tFunction))
def executeReadyFunctions(self):
if len(self.todo) > 0:
tFunction = heapq.heappop(self.todo)[1]
while tFunction and datetime.now() > tFunction.startTime:
#execute all the functions that are ready
tFunction.execute()
if len(self.todo) > 0:
tFunction = heapq.heappop(self.todo)[1]
else:
tFunction = None
if tFunction:
#this one's not ready yet, push it back on
heapq.heappush(self.todo, (tFunction.startTime, tFunction))
def singleArgFunction(x):
print str(x)
def multiArgFunction(x, y):
#Demonstration of passing multiple-argument functions
print str(x*y)
# Make some TimeoutFunction objects
# timeout is in milliseconds
a = TimeoutFunction(singleArgFunction, 1000, 20)
b = TimeoutFunction(multiArgFunction, 2000, *(11,12))
c = TimeoutFunction(quit, 3000, None)
todoList = TodoList()
todoList.addToList(a)
todoList.addToList(b)
todoList.addToList(c)
while True:
todoList.executeReadyFunctions()
===
In practice, you would likely have more going on in that while loop than just checking if your timeout functions are ready to go. You might be polling for user input, controlling some hardware, reading data, etc.
There is a nice solution using asyncio in python 3:
import asyncio
def async_call_later(seconds, callback):
async def schedule():
await asyncio.sleep(seconds)
if asyncio.iscoroutinefunction(callback):
await callback()
else:
callback()
asyncio.ensure_future(schedule())
async def do_something_async():
await asyncio.sleep(0.5)
print('Now! async')
async def main():
print('Scheduling...')
async_call_later(3, do_something_async)
async_call_later(3, lambda: print('Now!'))
print('Waiting...')
await asyncio.sleep(4)
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
Note that the sleep and similar functions in python expect a number of seconds, so I have replicated that. But you can supply fractions if you need milliseconds. (e.g 0.5 => 500ms).
An advantage of this approach over asyncio.call_later is that call_later only works with a synchronous callback. This implementation awaits if the callback is a coroutine so it is slightly more robust.
You could use the call_later method of python3’s asyncio event loop. Below example will just work.
import asyncio
loop = asyncio.get_event_loop()
def callback():
print("callback")
loop.call_later(1, callback)
loop.call_later(1, callback)
async def main():
while True:
await asyncio.sleep(1)
loop.run_until_complete(main())
I have to do a program in python that needs to execute for some time and then (does not matter where it was executing) it must dump information to a file, close the file and then exit.
The behavior here is equivalent in JavaScript to using setTimeout(func, 1000000) where its first parameter (func) would be a pointer to the function with the exit code and its second parameter would be the time available to the program to execute.
I know how to make this program in C (using SO signals) but with python
You can use signals in python as well (unix only)
import signal, sys
# install a SIGALRM handler
def handler(signum, frame):
print "got signal, exiting"
sys.exit(1)
signal.signal(signal.SIGALRM, handler)
# emit SIGALRM after 5 secs
signal.setitimer(signal.ITIMER_REAL, 5)
# do stuff
i = 1
while True:
if i % 100000 == 0:
print i
i += 1
In practice, a Timer is probably the simplest way to do what you want.
This code will do the following:
- After 1 second, it prints “arg1 arg2”
- After 2 seconds, it prints “OWLS OWLS OWLS”
===
from threading import Timer
def twoArgs(arg1,arg2):
print arg1
print arg2
print ""
def nArgs(*args):
for each in args:
print each
#arguments:
#how long to wait (in seconds),
#what function to call,
#what gets passed in
r = Timer(1.0, twoArgs, ("arg1","arg2"))
s = Timer(2.0, nArgs, ("OWLS","OWLS","OWLS"))
r.start()
s.start()
===
The above code will most likely solve your problem.
But! There is alternative way, that doesn’t use multithreading. It works much more like Javascript, which is single-threaded.
For this single-thread version, all you need to do is store the function and its arguments in an object, along with the time at which the function should be run.
Once you have the object containing the function call and the timeout, just periodically check if the function is ready to execute.
The right way to do this is by making a priority queue to store all of the functions we want to run in the future, as shown in the code below.
Just like in Javascript, this approach makes no guarantee that the function will be run exactly on time. A function that takes a very long time to run will delay the functions after it. But it does guarantee that a function will be run no sooner than its timeout.
This code will do the following:
- After 1 second, it prints “20”
- After 2 seconds, it prints “132”
- After 3 seconds, it quits.
===
from datetime import datetime, timedelta
import heapq
# just holds a function, its arguments, and when we want it to execute.
class TimeoutFunction:
def __init__(self, function, timeout, *args):
self.function = function
self.args = args
self.startTime = datetime.now() + timedelta(0,0,0,timeout)
def execute(self):
self.function(*self.args)
# A "todo" list for all the TimeoutFunctions we want to execute in the future
# They are sorted in the order they should be executed, thanks to heapq
class TodoList:
def __init__(self):
self.todo = []
def addToList(self, tFunction):
heapq.heappush(self.todo, (tFunction.startTime, tFunction))
def executeReadyFunctions(self):
if len(self.todo) > 0:
tFunction = heapq.heappop(self.todo)[1]
while tFunction and datetime.now() > tFunction.startTime:
#execute all the functions that are ready
tFunction.execute()
if len(self.todo) > 0:
tFunction = heapq.heappop(self.todo)[1]
else:
tFunction = None
if tFunction:
#this one's not ready yet, push it back on
heapq.heappush(self.todo, (tFunction.startTime, tFunction))
def singleArgFunction(x):
print str(x)
def multiArgFunction(x, y):
#Demonstration of passing multiple-argument functions
print str(x*y)
# Make some TimeoutFunction objects
# timeout is in milliseconds
a = TimeoutFunction(singleArgFunction, 1000, 20)
b = TimeoutFunction(multiArgFunction, 2000, *(11,12))
c = TimeoutFunction(quit, 3000, None)
todoList = TodoList()
todoList.addToList(a)
todoList.addToList(b)
todoList.addToList(c)
while True:
todoList.executeReadyFunctions()
===
In practice, you would likely have more going on in that while loop than just checking if your timeout functions are ready to go. You might be polling for user input, controlling some hardware, reading data, etc.
There is a nice solution using asyncio in python 3:
import asyncio
def async_call_later(seconds, callback):
async def schedule():
await asyncio.sleep(seconds)
if asyncio.iscoroutinefunction(callback):
await callback()
else:
callback()
asyncio.ensure_future(schedule())
async def do_something_async():
await asyncio.sleep(0.5)
print('Now! async')
async def main():
print('Scheduling...')
async_call_later(3, do_something_async)
async_call_later(3, lambda: print('Now!'))
print('Waiting...')
await asyncio.sleep(4)
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
Note that the sleep and similar functions in python expect a number of seconds, so I have replicated that. But you can supply fractions if you need milliseconds. (e.g 0.5 => 500ms).
An advantage of this approach over asyncio.call_later is that call_later only works with a synchronous callback. This implementation awaits if the callback is a coroutine so it is slightly more robust.
You could use the call_later method of python3’s asyncio event loop. Below example will just work.
import asyncio
loop = asyncio.get_event_loop()
def callback():
print("callback")
loop.call_later(1, callback)
loop.call_later(1, callback)
async def main():
while True:
await asyncio.sleep(1)
loop.run_until_complete(main())