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python input() and print() in multithreading [duplicate]

This question already has an answer here:
Python: How to NOT wait for a thread to finish to carry on? [duplicate]
(1 answer)
Closed 7 months ago.
I am using win10 and python 3.7.3 32-bit
I am trying to achieve the following: every second a data read-out is performed an printed, while a control loop waits for user input to control a device. see my code:
import device
import threading
from threading import Lock
def print_data(t_start):
while True:
data=getData() # generic example
print(f'data: {data} at time {time.time()-t_start}')
def control_func():
while True:
in_ = input(' Press ENTER to switch ON, Press x to quit')
if in_ == '':
device.on()
print('device ON')
elif in_ == 'x' or in_ == 'X':
device.off()
sys.exit()
else: continue
in_ = input(' Press ENTER to switch OFF, Press x to quit')
if in_ == '':
device.off()
print('device OFF')
elif in_ == 'x' or in_ == 'X':
device.off()
sys.exit()
else: continue
t_start = time.time()
device=device()
trd1 = threading.Thread(target=control_func())
trd2 = threading.Thread(target=print_data(t_start))
trd1.start() # starting the thread 1
trd2.start() # starting the thread 2
trd1.join()
trd2.join()
This only gives me the input statements from control_func() or the prints from print_data()
Same wih using mutliprocessing.
I didn't manage to make the two functions run simultanously.
replacing print() with
s_print_lock = Lock()
# Define a function to call print with the Lock
def s_print(*a, **b):
"""Thread safe print function"""
with s_print_lock:
print(*a, **b)
also didn't do the trick.
Since I am noob, please help. Or should I do a different approach all together?

You called the functions in the process of creating the Thread, you didn't pass the functions to the Thread for it to execute, so no actual work occurred in the threads. Change the Thread creation to:
trd1 = threading.Thread(target=control_func) # Remove call parens
trd2 = threading.Thread(target=print_data, args=(t_start,)) # Remove call parens and pass args tuple separately
so the functions themselves are passed, and the Thread actually runs them in the separate logical thread of execution. As written, you ran control_func to completion, then ran print_data to completion, then launched two Threads with target=None (the returned value from both functions) which do nothing, then joined the Threads doing nothing.
Additional notes:
If you're using multiprocessing, make sure to use multiprocessing.Lock, not threading.Lock (the latter is only guaranteed to work within a single process)
While the threaded cases likely doesn't require a lock (at least on CPython where the GIL protects against issues) if you're only printing a single atomic thing at a time, for multiprocessing you should definitely use the lock and add flush=True to all prints; without the flush=True, the actual output might be delayed indefinitely.
You need to provide some means to communicate that the loop is done to print_data; as written, control_func will sys.exit(), but that only exits that thread, not the program. Unless getData will somehow throw an exception as a result of control_func exiting, print_data will never exit, and therefore neither will the main thread.
Solutions for #3 include:
Using a threading.Event(); make a global should_stop = threading.Event(), change the print_data loop to while not should_stop.is_set():, and after trd1.join() returns, have the main thread call should_stop.set()
Make trd2 a daemon thread, and don't bother to join it (assumes it can die immediately when trd1 and the main thread have ended; might not hold for your scenario); it will die forcefully when all non-daemon threads have exited.

Python Threading timer with package [duplicate]

This question already has answers here:
How to repeatedly execute a function every x seconds?
(22 answers)
Closed 6 years ago.
I've been reading up on threading and tried implementing it into my code however I'm not sure if the way I'm doing it, is the best practise.
My code simply imports a self scripted package which pulls weather data and runs the package every 60 seconds thereafter.
I plan on running multiple packages which gather data at once, when I have worked out a good code technique.
from package.weather import weatherapi
import threading
def update():
weatherapi()
threading.Timer(60, update).start()
update()
Firstly it just seems messy and if I wanted more packages running in a thread, I'd need to create another update function
Secondly I'm not able to kill my process
If anyone has any suggestions, it would be greatly appreciated.

This is a really bad use of Threading.timer. You're constantly starting new threads, when you just want one thread to do something regularly. This code is equivalent:
from package.weather import weatherapi
import threading
import time
def update():
while True:
weatherapi()
time.sleep(60)
WHEATHER_THREAD=threading.Thread(target=update)
WHEATHER_THREAD.daemon = True # main can exit while thread is still running
WHEATHER_THREAD.start()
Since threads all use the same namespace, you can also make do with just one function.
UPDATE_CALLABLES = [weatherapi] # add new functions to have them called by update
def update():
while True:
for func in UPDATE_CALLABLES:
func()
time.sleep(60)
Note that UPDATE_CALLABLES can also be appended while the Thread is already running.

A class like this does what you want:
import threading
class Interval:
def __init__(self):
self.api=[]
self.interval=60
self.timer=self
def set_api(self,api):
self.api=api
def set_interval(self,interval):
self.interval=interval
def cancel(self):
pass
def stop(self):
self.timer.cancel()
def update(self):
for api in self.api:
api()
self.timer = threading.Timer(self.interval,self.update).start()
# Create instance and start with default parameters
interval=Interval()
interval.update()
# Later on change the list of items to call
interval.set_api([thisApi,thatApi])
# Later on still change the interval between calls
interval.set_interval(30)
# When you have had enough, cancel the timer
interval.stop()
Note that it still creates a new thread for each interval timed, but you can change the list of calls made at any time and stop it repeating at any time.

Python 'print' in a c++ based threading model

I am designing a Python app by calling a C++ DLL, I have posted my interaction between my DLL and Python 3.4 here. But now I need to do some process in streaming involving a threading based model and my callback function looks to put in a queue all the prints and only when my streaming has ended, all the Info is printed.
def callbackU(OutList, ConList, nB):
for i in range(nB):
out_list_item = cast(OutList[i], c_char_p).value
print("{}\t{}".format(ConList[i], out_list_item))
return 0
I have tried to use the next ways, but all of them looks to work in the same way:
from threading import Lock
print_lock = Lock()
def save_print(*args, **kwargs):
with print_lock:
print (*args, **kwargs)
def callbackU(OutList, ConList, nB):
for i in range(nB):
out_list_item = cast(OutList[i], c_char_p).value
save_print(out_list_item))
return 0
and:
import sys
def callbackU(OutList, ConList, nB):
for i in range(nB):
a = cast(OutList[i], c_char_p).value
sys.stdout.write(a)
sys.stdout.flush()
return 0
I would like that my callback prints its message when the it is called, not when the whole process ends.

I can find what was the problem, I am using a thread based process that needs to stay for an indefinite time before end it. In c++ I'm using getchar() to wait until the process has to be ended, then when I pushed the enter button the process jump to the releasing part. I also tried to use sleep()s of 0.5 secs in a while until a definite time has passed to test if that could help me, but it didn't. Both methods worked in the same way in my Python application, the values that I needed to have in streaming were put in a queue first and unless the process ended that values were printed.
The solution was to make two functions, the former one for initialize the thread based model. And the last one function for ends the process. By so doing I didn't need a getchar() neither a sleep(). This works pretty good to me!, thanks for you attention!

Python threading design

I'm trying to write a mini-game that allows me to practice my python threading skill. The game itself involves with timed bombs and citys that have them.
Here is my code:
class City(threading.Thread):
def __init__(self, name):
super().__init__()
self.name = name
self.bombs = None
self.activeBomb = None
self.bombID = 0
self.exploded = False
def addBomb(self, name, time, puzzle, answer, hidden=False):
self.bombs.append(Bomb(name, self.bombID, time, puzzle, answer, hidden))
self.activeBomb.append(self.bombID)
self.bombID += 1
def run(self):
for b in self.bombs:
b.start()
while True:
# listen to the bombs in the self.bombs # The part that I dont know how
# if one explodes
# print(self.name + ' has been destroyed')
# break
# if one is disarmed
# remove the bombID from the activeBomb
# if all bombs are disarmed (no activeBomb left)
# print('The city of ' + self.name + ' has been cleansed')
# break
class Bomb(threading.Thread):
def __init__(self, name, bombID, time, puzzle, answer, hidden=False):
super(Bomb, self).__init__()
self.name = name
self.bombID = bombID
self._timer = time
self._MAXTIME = time
self._disarmed = False
self._puzzle = puzzle
self._answer = answer
self._denoted = False
self._hidden = hidden
def run(self):
# A bomb goes off!!
if not self._hidden:
print('You have ' + str(self._MAXTIME)
+ ' seconds to solve the puzzle!')
print(self._puzzle)
while True:
if self._denoted:
print('BOOM')
// Communicate to city that bomb is denoted
break
elif not self._disarmed:
if self._timer == 0:
self._denoted = True
else:
self._timer -= 1
sleep(1)
else:
print('You have successfully disarmed bomb ' + str(self.name))
// Communicate to city that this bomb is disarmed
break
def answerPuzzle(self, ans):
print('Is answer ' + str(ans) + ' ?')
if ans == self._answer:
self._disarmed = True
else:
self._denotaed = True
def __eq__(self, bomb):
return self.bombID == bomb.bombID
def __hash__(self):
return id(self)
I currently don't know what is a good way for the City class to effectively keep track of the
bomb status.
The first thought I had was to use a for loop to have the City to check all the bombs in the
City, but I found it being too stupid and inefficient
So here is the question:
What is the most efficient way of implementing the bomb and City so that the city immediately know the state change of a bomb without having to check it every second?
PS: I do NOT mean to use this program to set off real bomb, so relax :D

A good case to use queue. Here is an example of the so-called producer - consumer pattern.
The work threads will run forever till your main program is done (that is what the daemon part and the "while True" is for). They will diligently monitor the in_queue for work packages. They will process the package until none is left. So when the in_queue is joined, your work threads' jobs are done. The out_queue here is an optional downstream processing step. So you can assemble the pieces from the work threads to a summary form. Useful when they are in a function.
If you need some outputs, like each work thread will print the results out to the screen or write to one single file, don't forget to use semaphore! Otherwise, your output will stumble onto each other.
Good luck!
from threading import Thread
import Queue
in_queue = Queue.Queue()
out_queue = Queue.Queue()
def work():
while True:
try:
sonId = in_queue.get()
###do your things here
result = sonID + 1
###you can even put your thread results again in another queue here
out_queue.put(result) ###optional
except:
pass
finally:
in_queue.task_done()
for i in range(20):
t = Thread(target=work)
t.daemon = True
t.start()
for son in range(10):
in_queue.put(son)
in_queue.join()
while not out_queue.empty():
result = out_queue.get()
###do something with your result here
out_queue.task_done()
out_queue.join()

The standard way of doing something like this is to use a queue - one thread watches the queue and waits for an object to handle (allowing it to idle happily), and the other thread pushes items onto the queue.
Python has the queue module (Queue in 2.x). Construct a queue in your listener thread and get() on it - this will block until something gets put on.
In your other thread, when a relevant event occurs, push it onto the queue and the listener thread will wake up and handle it. If you do this in a loop, you have the behaviour you want.

The easiest way would be to use a scheduler library. E.g. https://docs.python.org/2/library/sched.html. Using this you can simply schedule bombs to call a function or method at the time they go off. This is what I would recommend if you did not wanted to learn about threads.
E.g.
import sched
s = sched.scheduler(time.time, time.sleep)
class Bomb():
def explode(self):
if not self._disarmed:
print "BOOM"
def __init__(self, time):
s.enter(self._MAXTIME, 1, self.explode)
However, that way you will not learn about threads.
If you really want to use threads directly, then you can simply let the bombs call sleep until it is their time to go off. E.g.
class Bomb(threading.Thread)
def run(self):
time.sleep.(self._MAXTIME)
if not self._disarmed:
print "BOOM"
However, this is not a nice way to handle threads, since the threads will block your application. You will not be able to exit the application until you stop the threads. You can avoid this by making the thread a daemon thread. bomb.daemon = True.
In some cases, the best way to handle this is to actually "wake up" each second and check the status of the world. This may be the case when you need to perform some cleanup actions when the thread is stopped. E.g. You may need to close a file. Checking each second may seem wasteful, but it is actually the proper way to handle such problems. Modern desktop computers are mostly idle. To be interrupted for a few milliseconds each second will not cause them much sweat.
class Bomb(threading.Thread)
def run(self):
while not self._disarmed:
if time.now() > self.time_to_explode:
print "BOOM"
break
else:
time.sleep.(1)

Before you start "practising threading with python", I think it is important to understand Python threading model - it is Java threading model, but comes with a more restrictive option:
https://docs.python.org/2/library/threading.html
The design of this module is loosely based on Java’s threading model.
However, where Java makes locks and condition variables basic behavior
of every object, they are separate objects in Python. Python’s Thread
class supports a subset of the behavior of Java’s Thread class;
currently, there are no priorities, no thread groups, and threads
cannot be destroyed, stopped, suspended, resumed, or interrupted. The
static methods of Java’s Thread class, when implemented, are mapped to
module-level functions.
Locks being in separate objects, and not per-object, following the diagram below, means less independent scheduling even when different objects are accessed - because possibly even same locks are necessary.
For some python implementation - threading is not really fully concurrent:
http://uwpce-pythoncert.github.io/EMC-Python300-Spring2015/html_slides/07-threading-and-multiprocessing.html#slide-5
A thread is the entity within a process that can be scheduled for
execution
Threads are lightweight processes, run in the address space of an OS
process.
These threads share the memory and the state of the process. This
allows multiple threads access to data in the same scope.
Python threads are true OS level threads
Threads can not gain the performance advantage of multiple processors
due to the Global Interpreter Lock (GIL)
http://uwpce-pythoncert.github.io/EMC-Python300-Spring2015/html_slides/07-threading-and-multiprocessing.html#slide-6
And this (from above slide):

How can I reproduce the race conditions in this python code reliably?

Context
I recently posted a timer class for review on Code Review. I'd had a gut feeling there were concurrency bugs as I'd once seen 1 unit test fail, but was unable to reproduce the failure. Hence my post to code review.
I got some great feedback highlighting various race conditions in the code. (I thought) I understood the problem and the solution, but before making any fixes, I wanted to expose the bugs with a unit test. When I tried, I realised it was difficult. Various stack exchange answers suggested I'd have to control the execution of threads to expose the bug(s) and any contrived timing would not necessarily be portable to a different machine. This seemed like a lot of accidental complexity beyond the problem I was trying to solve.
Instead I tried using the best static analysis (SA) tool for python, PyLint, to see if it'd pick out any of the bugs, but it couldn't. Why could a human find the bugs through code review (essentially SA), but a SA tool could not?
Afraid of trying to get Valgrind working with python (which sounded like yak-shaving), I decided to have a bash at fixing the bugs without reproducing them first. Now I'm in a pickle.
Here's the code now.
from threading import Timer, Lock
from time import time
class NotRunningError(Exception): pass
class AlreadyRunningError(Exception): pass
class KitchenTimer(object):
'''
Loosely models a clockwork kitchen timer with the following differences:
You can start the timer with arbitrary duration (e.g. 1.2 seconds).
The timer calls back a given function when time's up.
Querying the time remaining has 0.1 second accuracy.
'''
PRECISION_NUM_DECIMAL_PLACES = 1
RUNNING = "RUNNING"
STOPPED = "STOPPED"
TIMEUP = "TIMEUP"
def __init__(self):
self._stateLock = Lock()
with self._stateLock:
self._state = self.STOPPED
self._timeRemaining = 0
def start(self, duration=1, whenTimeup=None):
'''
Starts the timer to count down from the given duration and call whenTimeup when time's up.
'''
with self._stateLock:
if self.isRunning():
raise AlreadyRunningError
else:
self._state = self.RUNNING
self.duration = duration
self._userWhenTimeup = whenTimeup
self._startTime = time()
self._timer = Timer(duration, self._whenTimeup)
self._timer.start()
def stop(self):
'''
Stops the timer, preventing whenTimeup callback.
'''
with self._stateLock:
if self.isRunning():
self._timer.cancel()
self._state = self.STOPPED
self._timeRemaining = self.duration - self._elapsedTime()
else:
raise NotRunningError()
def isRunning(self):
return self._state == self.RUNNING
def isStopped(self):
return self._state == self.STOPPED
def isTimeup(self):
return self._state == self.TIMEUP
#property
def timeRemaining(self):
if self.isRunning():
self._timeRemaining = self.duration - self._elapsedTime()
return round(self._timeRemaining, self.PRECISION_NUM_DECIMAL_PLACES)
def _whenTimeup(self):
with self._stateLock:
self._state = self.TIMEUP
self._timeRemaining = 0
if callable(self._userWhenTimeup):
self._userWhenTimeup()
def _elapsedTime(self):
return time() - self._startTime
Question
In the context of this code example, how can I expose the race conditions, fix them, and prove they're fixed?
Extra points
extra points for a testing framework suitable for other implementations and problems rather than specifically to this code.
Takeaway
My takeaway is that the technical solution to reproduce the identified race conditions is to control the synchronism of two threads to ensure they execute in the order that will expose a bug. The important point here is that they are already identified race conditions. The best way I've found to identify race conditions is to put your code up for code review and encourage more expert people analyse it.

Traditionally, forcing race conditions in multithreaded code is done with semaphores, so you can force a thread to wait until another thread has achieved some edge condition before continuing.
For example, your object has some code to check that start is not called if the object is already running. You could force this condition to make sure it behaves as expected by doing something like this:
starting a KitchenTimer
having the timer block on a semaphore while in the running state
starting the same timer in another thread
catching AlreadyRunningError
To do some of this you may need to extend the KitchenTimer class. Formal unit tests will often use mock objects which are defined to block at critical times. Mock objects are a bigger topic than I can address here, but googling "python mock object" will turn up a lot of documentation and many implementations to choose from.
Here's a way that you could force your code to throw AlreadyRunningError:
import threading
class TestKitchenTimer(KitchenTimer):
_runningLock = threading.Condition()
def start(self, duration=1, whenTimeUp=None):
KitchenTimer.start(self, duration, whenTimeUp)
with self._runningLock:
print "waiting on _runningLock"
self._runningLock.wait()
def resume(self):
with self._runningLock:
self._runningLock.notify()
timer = TestKitchenTimer()
# Start the timer in a subthread. This thread will block as soon as
# it is started.
thread_1 = threading.Thread(target = timer.start, args = (10, None))
thread_1.start()
# Attempt to start the timer in a second thread, causing it to throw
# an AlreadyRunningError.
try:
thread_2 = threading.Thread(target = timer.start, args = (10, None))
thread_2.start()
except AlreadyRunningError:
print "AlreadyRunningError"
timer.resume()
timer.stop()
Reading through the code, identify some of the boundary conditions you want to test, then think about where you would need to pause the timer to force that condition to arise, and add Conditions, Semaphores, Events, etc. to make it happen. e.g. what happens if, just as the timer runs the whenTimeUp callback, another thread tries to stop it? You can force that condition by making the timer wait as soon as it's entered _whenTimeUp:
import threading
class TestKitchenTimer(KitchenTimer):
_runningLock = threading.Condition()
def _whenTimeup(self):
with self._runningLock:
self._runningLock.wait()
KitchenTimer._whenTimeup(self)
def resume(self):
with self._runningLock:
self._runningLock.notify()
def TimeupCallback():
print "TimeupCallback was called"
timer = TestKitchenTimer()
# The timer thread will block when the timer expires, but before the callback
# is invoked.
thread_1 = threading.Thread(target = timer.start, args = (1, TimeupCallback))
thread_1.start()
sleep(2)
# The timer is now blocked. In the parent thread, we stop it.
timer.stop()
print "timer is stopped: %r" % timer.isStopped()
# Now allow the countdown thread to resume.
timer.resume()
Subclassing the class you want to test isn't an awesome way to instrument it for testing: you'll have to override basically all of the methods in order to test race conditions in each one, and at that point there's a good argument to be made that you're not really testing the original code. Instead, you may find it cleaner to put the semaphores right in the KitchenTimer object but initialized to None by default, and have your methods check if testRunningLock is not None: before acquiring or waiting on the lock. Then you can force races on the actual code that you're submitting.
Some reading on Python mock frameworks that may be helpful. In fact, I'm not sure that mocks would be helpful in testing this code: it's almost entirely self-contained and doesn't rely on many external objects. But mock tutorials sometimes touch on issues like these. I haven't used any of these, but the documentation on these like a good place to get started:
Getting Started with Mock
Using Fudge
Python Mock Testing Techniques and Tools

The most common solution to testing thread (un)safe code is to start a lot of threads and hope for the best. The problem I, and I can imagine others, have with this is that it relies on chance and it makes tests 'heavy'.
As I ran into this a while ago I wanted to go for precision instead of brute force. The result is a piece of test code to cause race-conditions by letting the threads race neck to neck.
Sample racey code
spam = []
def set_spam():
spam[:] = foo()
use(spam)
If set_spam is called from several threads, a race condition exists between modification and use of spam. Let's try to reproduce it consistently.
How to cause race-conditions
class TriggeredThread(threading.Thread):
def __init__(self, sequence=None, *args, **kwargs):
self.sequence = sequence
self.lock = threading.Condition()
self.event = threading.Event()
threading.Thread.__init__(self, *args, **kwargs)
def __enter__(self):
self.lock.acquire()
while not self.event.is_set():
self.lock.wait()
self.event.clear()
def __exit__(self, *args):
self.lock.release()
if self.sequence:
next(self.sequence).trigger()
def trigger(self):
with self.lock:
self.event.set()
self.lock.notify()
Then to demonstrate the use of this thread:
spam = [] # Use a list to share values across threads.
results = [] # Register the results.
def set_spam():
thread = threading.current_thread()
with thread: # Acquires the lock.
# Set 'spam' to thread name
spam[:] = [thread.name]
# Thread 'releases' the lock upon exiting the context.
# The next thread is triggered and this thread waits for a trigger.
with thread:
# Since each thread overwrites the content of the 'spam'
# list, this should only result in True for the last thread.
results.append(spam == [thread.name])
threads = [
TriggeredThread(name='a', target=set_spam),
TriggeredThread(name='b', target=set_spam),
TriggeredThread(name='c', target=set_spam)]
# Create a shifted sequence of threads and share it among the threads.
thread_sequence = itertools.cycle(threads[1:] + threads[:1])
for thread in threads:
thread.sequence = thread_sequence
# Start each thread
[thread.start() for thread in threads]
# Trigger first thread.
# That thread will trigger the next thread, and so on.
threads[0].trigger()
# Wait for each thread to finish.
[thread.join() for thread in threads]
# The last thread 'has won the race' overwriting the value
# for 'spam', thus [False, False, True].
# If set_spam were thread-safe, all results would be true.
assert results == [False, False, True], "race condition triggered"
assert results == [True, True, True], "code is thread-safe"
I think I explained enough about this construction so you can implement it for your own situation. I think this fits the 'extra points' section quite nicely:
extra points for a testing framework suitable for other implementations and problems rather than specifically to this code.
Solving race-conditions
Shared variables
Each threading issue is solved in it's own specific way. In the example above I caused a race-condition by sharing a value across threads. Similar problems can occur when using global variables, such as a module attribute. The key to solving such issues may be to use a thread-local storage:
# The thread local storage is a global.
# This may seem weird at first, but it isn't actually shared among threads.
data = threading.local()
data.spam = [] # This list only exists in this thread.
results = [] # Results *are* shared though.
def set_spam():
thread = threading.current_thread()
# 'get' or set the 'spam' list. This actually creates a new list.
# If the list was shared among threads this would cause a race-condition.
data.spam = getattr(data, 'spam', [])
with thread:
data.spam[:] = [thread.name]
with thread:
results.append(data.spam == [thread.name])
# Start the threads as in the example above.
assert all(results) # All results should be True.
Concurrent reads/writes
A common threading issue is the problem of multiple threads reading and/or writing to a data holder concurrently. This problem is solved by implementing a read-write lock. The actual implementation of a read-write lock may differ. You may choose a read-first lock, a write-first lock or just at random.
I'm sure there are examples out there describing such locking techniques. I may write an example later as this is quite a long answer already. ;-)
Notes
Have a look at the threading module documentation and experiment with it a bit. As each threading issue is different, different solutions apply.
While on the subject of threading, have a look at the Python GIL (Global Interpreter Lock). It is important to note that threading may not actually be the best approach in optimizing performance (but this is not your goal). I found this presentation pretty good: https://www.youtube.com/watch?v=zEaosS1U5qY

You can test it by using a lot of threads:
import sys, random, thread
def timeup():
sys.stdout.write("Timer:: Up %f" % time())
def trdfunc(kt, tid):
while True :
sleep(1)
if not kt.isRunning():
if kt.start(1, timeup):
sys.stdout.write("[%d]: started\n" % tid)
else:
if random.random() < 0.1:
kt.stop()
sys.stdout.write("[%d]: stopped\n" % tid)
sys.stdout.write("[%d] remains %f\n" % ( tid, kt.timeRemaining))
kt = KitchenTimer()
kt.start(1, timeup)
for i in range(1, 100):
thread.start_new_thread ( trdfunc, (kt, i) )
trdfunc(kt, 0)
A couple of problem problems I see:
When a thread sees the timer as not running and try to start it, the
code generally raises an exception due to context switch in between
test and start. I think raising an exception is too much. Or you can
have an atomic testAndStart function
A similar problem occurs with stop. You can implement a testAndStop
function.
Even this code from the timeRemaining function:
if self.isRunning():
self._timeRemaining = self.duration - self._elapsedTime()
Needs some sort of atomicity, perhaps you need to grab a lock before
testing isRunning.
If you plan to share this class between threads, you need to address these issues.

In general - this is not viable solution. You can reproduce this race condition by using debugger (set breakpoints in some locations in the code, than, when it hits one of the breakpoints - freeze the thread and run the code until it hits another breakpoint, then freeze this thread and unfreeze the first thread, you can interleave threads execution in any way using this technique).
The problem is - the more threads and code you have, the more ways to interleave side effects they will have. Actually - it will grow exponentially. There is no viable solution to test it in general. It is possible only in some simple cases.
The solution to this problem are well known. Write code that is aware of it's side effects, control side effects with synchronisation primitives like locks, semaphores or queues or use immutable data if its possible.
Maybe more practical way is to use runtime checks to force correct call order. For example (pseudocode):
class RacyObject:
def __init__(self):
self.__cnt = 0
...
def isReadyAndLocked(self):
acquire_object_lock
if self.__cnt % 2 != 0:
# another thread is ready to start the Job
return False
if self.__is_ready:
self.__cnt += 1
return True
# Job is in progress or doesn't ready yet
return False
release_object_lock
def doJobAndRelease(self):
acquire_object_lock
if self.__cnt % 2 != 1:
raise RaceConditionDetected("Incorrect order")
self.__cnt += 1
do_job()
release_object_lock
This code will throw exception if you doesn't check isReadyAndLock before calling doJobAndRelease. This can be tested easily using only one thread.
obj = RacyObject()
...
# correct usage
if obj.isReadyAndLocked()
obj.doJobAndRelease()