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159 lines
6.2 KiB
Python
159 lines
6.2 KiB
Python
"""A generally useful event scheduler class.
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Each instance of this class manages its own queue.
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No multi-threading is implied; you are supposed to hack that
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yourself, or use a single instance per application.
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Each instance is parametrized with two functions, one that is
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supposed to return the current time, one that is supposed to
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implement a delay. You can implement real-time scheduling by
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substituting time and sleep from built-in module time, or you can
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implement simulated time by writing your own functions. This can
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also be used to integrate scheduling with STDWIN events; the delay
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function is allowed to modify the queue. Time can be expressed as
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integers or floating point numbers, as long as it is consistent.
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Events are specified by tuples (time, priority, action, argument).
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As in UNIX, lower priority numbers mean higher priority; in this
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way the queue can be maintained as a priority queue. Execution of the
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event means calling the action function, passing it the argument
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sequence in "argument" (remember that in Python, multiple function
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arguments are be packed in a sequence).
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The action function may be an instance method so it
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has another way to reference private data (besides global variables).
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"""
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# XXX The timefunc and delayfunc should have been defined as methods
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# XXX so you can define new kinds of schedulers using subclassing
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# XXX instead of having to define a module or class just to hold
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# XXX the global state of your particular time and delay functions.
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import time
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import heapq
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from collections import namedtuple
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try:
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import threading
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except ImportError:
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import dummy_threading as threading
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__all__ = ["scheduler"]
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class Event(namedtuple('Event', 'time, priority, action, argument, kwargs')):
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def __eq__(s, o): return (s.time, s.priority) == (o.time, o.priority)
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def __ne__(s, o): return (s.time, s.priority) != (o.time, o.priority)
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def __lt__(s, o): return (s.time, s.priority) < (o.time, o.priority)
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def __le__(s, o): return (s.time, s.priority) <= (o.time, o.priority)
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def __gt__(s, o): return (s.time, s.priority) > (o.time, o.priority)
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def __ge__(s, o): return (s.time, s.priority) >= (o.time, o.priority)
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class scheduler:
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def __init__(self, timefunc=time.time, delayfunc=time.sleep):
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"""Initialize a new instance, passing the time and delay
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functions"""
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self._queue = []
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self._lock = threading.RLock()
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self.timefunc = timefunc
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self.delayfunc = delayfunc
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def enterabs(self, time, priority, action, argument=[], kwargs={}):
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"""Enter a new event in the queue at an absolute time.
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Returns an ID for the event which can be used to remove it,
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if necessary.
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"""
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with self._lock:
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event = Event(time, priority, action, argument, kwargs)
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heapq.heappush(self._queue, event)
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return event # The ID
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def enter(self, delay, priority, action, argument=[], kwargs={}):
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"""A variant that specifies the time as a relative time.
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This is actually the more commonly used interface.
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"""
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with self._lock:
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time = self.timefunc() + delay
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return self.enterabs(time, priority, action, argument, kwargs)
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def cancel(self, event):
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"""Remove an event from the queue.
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This must be presented the ID as returned by enter().
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If the event is not in the queue, this raises ValueError.
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"""
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with self._lock:
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self._queue.remove(event)
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heapq.heapify(self._queue)
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def empty(self):
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"""Check whether the queue is empty."""
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with self._lock:
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return not self._queue
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def run(self, blocking=True):
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"""Execute events until the queue is empty.
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If blocking is False executes the scheduled events due to
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expire soonest (if any) and then return the deadline of the
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next scheduled call in the scheduler.
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When there is a positive delay until the first event, the
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delay function is called and the event is left in the queue;
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otherwise, the event is removed from the queue and executed
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(its action function is called, passing it the argument). If
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the delay function returns prematurely, it is simply
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restarted.
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It is legal for both the delay function and the action
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function to modify the queue or to raise an exception;
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exceptions are not caught but the scheduler's state remains
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well-defined so run() may be called again.
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A questionable hack is added to allow other threads to run:
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just after an event is executed, a delay of 0 is executed, to
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avoid monopolizing the CPU when other threads are also
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runnable.
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"""
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# localize variable access to minimize overhead
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# and to improve thread safety
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with self._lock:
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q = self._queue
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delayfunc = self.delayfunc
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timefunc = self.timefunc
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pop = heapq.heappop
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while q:
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time, priority, action, argument, kwargs = checked_event = q[0]
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now = timefunc()
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if now < time:
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if not blocking:
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return time - now
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delayfunc(time - now)
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else:
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event = pop(q)
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# Verify that the event was not removed or altered
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# by another thread after we last looked at q[0].
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if event is checked_event:
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action(*argument, **kwargs)
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delayfunc(0) # Let other threads run
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else:
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heapq.heappush(q, event)
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@property
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def queue(self):
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"""An ordered list of upcoming events.
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Events are named tuples with fields for:
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time, priority, action, arguments
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"""
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# Use heapq to sort the queue rather than using 'sorted(self._queue)'.
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# With heapq, two events scheduled at the same time will show in
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# the actual order they would be retrieved.
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with self._lock:
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events = self._queue[:]
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return map(heapq.heappop, [events]*len(events))
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