hiro 0.0.2

time manipulation utilities for python

Hiro context managers

Timeline context

The hiro.Timeline context manager hijacks a few commonly used time functions to allow time manipulation within its context. Specifically time.sleep, time.time, time.gmtime, datetime.now, datetime.utcnow and datetime.today behave according the configuration of the context.

The context provides the following manipulation options:

• rewind: accepts seconds as an integer or a timedelta object.

• forward: accepts seconds as an integer or a timedelta object.

• freeze: accepts a floating point time since epoch or datetime or date object to freeze the time at.

• unfreeze: resumes time from the point it was frozen at.

• scale: accepts a floating point to accelerate/decelerate time by. > 1 = acceleration, < 1 = deceleration

• reset: resets all time alterations.

import hiro
from datetime import timedelta, datetime
import time

datetime.now().isoformat()
# OUT: '2013-11-30T10:45:09.583797'
with hiro.Timeline() as timeline:

    # forward by an hour
    timeline.forward(60*60)
    datetime.now().isoformat()
    # OUT: '2013-11-30T11:45:09.585100'

    # jump forward by 10 minutes
    timeline.forward(timedelta(minutes=10))
    datetime.now().isoformat()
    # OUT: '2013-11-30T11:55:09.585115'

    # jump to yesterday and freeze forward/reverse
    timeline.freeze(datetime.now() - timedelta(days=-1))
    datetime.now().isoformat()
    # OUT: '2013-12-01T13:05:09'

    timeline.scale(5) # scale time by 5x
    time.sleep(5) # this will effectively only sleep for 1 second

    datetime.now().isoformat()
    # OUT: '2013-12-01T13:05:09'

    timeline.reverse(timedelta(year=1))

    print datetime.now().isoformat()
    # OUT: '2013-12-01T13:05:09'

Scaled Timeline Context

The ScaledTimeline context behaves identically to the Timeline context with the one exception that it can be initialized with a default scale factor

import hiro
from datetime import timedelta, datetime, date
import time

# all time operations will occur at 50000x
with hiro.ScaledTimeline(factor=50000):
    datetime.now().isoformat()
    # OUT: '2013-11-30T12:37:56.051953'

    # sleep for an hour
    time.sleep(60*60) # effectively 72 ms

    datetime.now().isoformat()
    # OUT: '2013-11-30T13:38:32.447884'

    # sleep for a day
    time.sleep(60*60*24) # effectively 1.7 seconds

    date.today().isoformat()
    # OUT: '2013-12-01'

Hiro executors

In order to execute certain callables within a ScaledTimeline context, two shortcut functions are provided.

• run_sync(factor=1, callable, *args, **kwargs)

• run_async(factor=1, callable, *args, **kwargs)

Both functions return a ScaledRunner object which provides the following methods

• get_execution_time: The actual execution time of the callable

• get_response (will either return the actual return value of callable or raise the exception that was thrown)

run_async returns a derived class of ScaledRunner that additionally provides the following methods

• is_running: True/False depending on whether the callable has completed execution

• join: blocks until the callable completes execution

Example

import hiro
import time

def _slow_function(n):
    time.sleep(n)
    if n > 10:
        raise RuntimeError()
    return n

runner = hiro.run_sync(10, _slow_function, 10)
runner.get_response()
# OUT: 10

# due to the scale factor 10 it only took 1s to execute
runner.get_execution_time()
# OUT: 1.1052658557891846

runner = hiro.run_async(10, _slow_function, 11)
runner.is_running()
# OUT: True
runner.join()
runner.get_execution_time()
# OUT: 1.1052658557891846
runner.get_response()
# OUT: Traceback (most recent call last):
# ....
# OUT:   File "<input>", line 4, in _slow_function
# OUT: RuntimeError

Yatta!

– Hiro Nakamura