Skip to main content

time manipulation utilities for python

Project description

travis coveralls pypi license

Yatta!

—Hiro Nakamura

Hiro context manager

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: '2013-12-01T06:55:41.706060'
with hiro.Timeline() as timeline:

    # forward by an hour
    timeline.forward(60*60)
    datetime.now().isoformat()
    # OUT: '2013-12-01T07:55:41.707383'

    # jump forward by 10 minutes
    timeline.forward(timedelta(minutes=10))
    datetime.now().isoformat()
    # OUT: '2013-12-01T08:05:41.707425'

    # jump to yesterday and freeze
    timeline.freeze(datetime.now() - timedelta(hours=24))
    datetime.now().isoformat()
    # OUT: '2013-11-30T09:15:41'

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

    # since time is frozen the sleep has no effect
    datetime.now().isoformat()
    # OUT: '2013-11-30T09:15:41'

    timeline.rewind(timedelta(days=365))

    datetime.now().isoformat()
    # OUT: '2012-11-30T09:15:41'

To reduce the amount of statements inside the context, certain timeline setup tasks can be done via the constructor and/or by using the fluent interface.

import hiro
import time
from datetime import timedelta, datetime

start_point = datetime(2012,12,12,0,0,0)
my_timeline = hiro.Timeline(scale=5).forward(60*60).freeze()
with my_timeline as timeline:
    print datetime.now()
    # OUT: '2012-12-12 01:00:00.000315'
    time.sleep(5) # effectively 1 second
    # no effect as time is frozen
    datetime.now()
    # OUT: '2012-12-12 01:00:00.000315'
    timeline.unfreeze()
    # back to starting point
    datetime.now()
    # OUT: '2012-12-12 01:00:00.000317'
    time.sleep(5) # effectively 1 second
    # takes effect (+5 seconds)
    datetime.now()
    # OUT: '2012-12-12 01:00:05.003100'

Timeline can additionally be used as a decorator

import hiro
import time, datetime

@hiro.Timeline(scale=50000)
def sleeper():
    datetime.datetime.now()
    # OUT: '2013-11-30 14:27:43.409291'
    time.sleep(60*60) # effectively 72 ms
    datetime.datetime.now()
    # OUT: '2013-11-30 15:28:36.240675'

@hiro.Timeline()
def sleeper_aware(timeline):
    datetime.datetime.now()
    # OUT: '2013-11-30 14:27:43.409291'
    timeline.forward(60*60)
    datetime.datetime.now()
    # OUT: '2013-11-30 15:28:36.240675'

Hiro executors

In order to execute certain callables within a Timeline 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hiro-0.3.tar.gz (9.4 kB view details)

Uploaded Source

Built Distribution

hiro-0.3-py2.7.egg (17.9 kB view details)

Uploaded Source

File details

Details for the file hiro-0.3.tar.gz.

File metadata

  • Download URL: hiro-0.3.tar.gz
  • Upload date:
  • Size: 9.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for hiro-0.3.tar.gz
Algorithm Hash digest
SHA256 33c0a32d709fc980d54b096f1b1055a18e8dffb8e307f8ac7543fae10ededbfd
MD5 c4e58ed0aabffa54d55cd4ca04e86b72
BLAKE2b-256 dd70c8fbecf1e4d161d9d8a7f6210bde3e986cebf7b18ad242b344358ec20782

See more details on using hashes here.

File details

Details for the file hiro-0.3-py2.7.egg.

File metadata

  • Download URL: hiro-0.3-py2.7.egg
  • Upload date:
  • Size: 17.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for hiro-0.3-py2.7.egg
Algorithm Hash digest
SHA256 996646d4d64f7914382b0c92e6d826b6326d3107f7f4e9f9d8cce8593f7e7d19
MD5 7d08e5af2b8a909fd2425190d3a8aed1
BLAKE2b-256 c6730962878c6256ecda0933c421a0d954d0696301ffed086795d45137921f77

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page