sisl.selector module

Sub-package to easily make algorithmic decisions based on different routines

Several functions exists here to most efficiently choose the most performant routine.

The Selector will run through the different routines and decide, based on all the calls which is the best one.

Basically the Selector will only be a powerful tool if a given routine is called numerous times.

The following example will show how the TimeSelector may be used to automatically call the fastest of 3 routines:

>>> def func1():
...    print('Func - 1')
>>> def func2():
...    print('Func - 2')
...    time.sleep(1)
>>> def func3():
...    print('Func - 3')
...    time.sleep(1)
>>> selector = TimeSelector([func1, func2, func3])
>>> selector()
Func - 1
>>> selector()
Func - 2
>>> selector()
Func - 3
>>> selector() # will now only call the fastest of the 3
Func - 1

In certain cases one may wish to limit the search for a selected routine by only searching until the performance of the next called routine drops. This is called an ordered selector because it tries them in order, and once one is slower than the former tested ones, it will not test any further. For the above same functions we may do:

>>> selector = TimeSelector([func1, func2, func3], ordered=True)
>>> selector()
Func - 1
>>> selector()
Func - 2
>>> selector()
Func - 1
>>> selector()
Func - 1

class sisl.selector.Selector(routines=None, ordered=False)

Bases: object

Base class for implementing a selector of class routines

This class should contain a list of routines and may then be used to always return the best performant routine.

This is done on a per-class basis where this class should initially determine which routine is the best performing one and then always return that one.

Attributes

routines	(list of func) this is a list of functions that will be selected from.
ordered	(bool) If False a simple selection of the most performant one will be chosen. If True, it will check the routines in order and once one of the routines is less performant it will choose from the setof runned routines.

Methods

__call__(*args, **kwargs)	Call the function that optimizes the run-time the most
append(routine)	Prepends a new routine to the selector
next()	Choose the next routine that requires performance analysis
prepend(routine)	Prepends a new routine to the selector
reset()	Reset the performance table to redo the performance checks
select_best([routine])	Update the best routine, if applicable
start()	Start the performance profiler
stop(start)	Stop the performance profiler

append(routine)

Prepends a new routine to the selector

Parameters:

routine : func

the new routine to be tested in the selector

best

next()

Choose the next routine that requires performance analysis

Returns:

int, func :

a tuple with the int specifying the routine index. func is the routine that is to be runned.

ordered

performances

prepend(routine)

Prepends a new routine to the selector

Parameters:

routine : func

the new routine to be tested in the selector

reset()

Reset the performance table to redo the performance checks

routines

select_best(routine=None)

Update the best routine, if applicable

Update the selector to choose the best method. If not all routines have been carried through, then no best routine will be selected (unless self.ordered is True).

By passing a routine as an argument that given routine will by default be the chosen best algorithm.

Parameters:

routine : func or str

If None is passed (the default) it will select the best default routine based on the stored performances. If, however, not all performance values has been created no routine will be selected.

If passing a func that function will be chosen as the best method

start()

Start the performance profiler

This routine should return an initial state value. The difference between stop() - start() should yield a performance identifier which may be used to control the used algorithm.

A large performance identifier results in the use of the routine.

stop(start)

Stop the performance profiler

This routine should return an initial state value. The difference between stop() - start() should yield a performance identifier which may be used to control the used algorithm.

A large performance identifier results in the use of the routine.

Parameters:

start : float

the output of the start() routine to convert to actual performance identifier

class sisl.selector.TimeSelector(routines=None, ordered=False)

Bases: sisl.selector.Selector

Routine performance selector based on timings for the routines

Attributes

best
ordered
performances
routines

Methods

__call__(*args, **kwargs)	Call the function that optimizes the run-time the most
append(routine)	Prepends a new routine to the selector
next()	Choose the next routine that requires performance analysis
prepend(routine)	Prepends a new routine to the selector
reset()	Reset the performance table to redo the performance checks
select_best([routine])	Update the best routine, if applicable
start()	Start the timing routine
stop(start)	Stop the timing routine

start()

Start the timing routine

stop(start)

Stop the timing routine