idmtools.utils.decorators module

class idmtools.utils.decorators.abstractstatic(function)

Bases: staticmethod

A decorator for defining a method both as static and abstract.

idmtools.utils.decorators.optional_decorator(decorator: Callable, condition: Union[bool, Callable[], bool]])

class idmtools.utils.decorators.SingletonMixin

Bases: object

classmethod instance()

idmtools.utils.decorators.cache_for(ttl=None)

idmtools.utils.decorators.optional_yaspin_load(*yargs, **ykwargs) → Callable

Adds a CLI spinner to a function if:

• yaspin package is present.

• NO_SPINNER environment variable is not defined.

Parameters

• *yargs – Arguments to pass to yaspin constructor.

• **ykwargs – Keyword arguments to pass to yaspin constructor.

Examples

@optional_yaspin_load(text="Loading test", color="yellow")
def test():
    time.sleep(100)

Returns

A callable wrapper function.

class idmtools.utils.decorators.ParallelizeDecorator(queue=None, pool_type: Optional[Type[concurrent.futures._base.Executor]] = <class 'concurrent.futures.thread.ThreadPoolExecutor'>)

Bases: object

ParallelizeDecorator allows you to easily parallelize a group of code. A simple of example would be

Examples

op_queue = ParallelizeDecorator()

class Ops:
    op_queue.parallelize
    def heavy_op():
        time.sleep(10)

    def do_lots_of_heavy():
        futures = [self.heavy_op() for i in range(100)]
        results = op_queue.get_results(futures)

parallelize(func)

join()

get_results(futures, ordered=False)