Skip to main content

time manipulation utilities for python

Project description

travis coveralls pypi

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 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-0.1.0.tar.gz (8.9 kB view details)

Uploaded Source

Built Distribution

hiro-0.1.0-py2.7.egg (30.1 kB view details)

Uploaded Source

File details

Details for the file hiro-0.1.0.tar.gz.

File metadata

  • Download URL: hiro-0.1.0.tar.gz
  • Upload date:
  • Size: 8.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for hiro-0.1.0.tar.gz
Algorithm Hash digest
SHA256 4f89daf30e427a21c4f1057d1e44e8ef8490f934357f72840dbc6d141301bbae
MD5 20875c9e28afc333654c4bd1a0599c2e
BLAKE2b-256 f7b5c8a1e3fc3d3537a74222ce6a6e86c9a1d806209d53083c26b36024a54c96

See more details on using hashes here.

File details

Details for the file hiro-0.1.0-py2.7.egg.

File metadata

  • Download URL: hiro-0.1.0-py2.7.egg
  • Upload date:
  • Size: 30.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for hiro-0.1.0-py2.7.egg
Algorithm Hash digest
SHA256 63a0ddc10f3441011ae06434c4f796861205948ae7f666ee9f6e6bac44d990e5
MD5 1248a994e15a8857df87dcbede72720e
BLAKE2b-256 3427d9b0d847b0d033f6dc6c1d8ad8c0d5816e9831b3587daaa88b57529e3247

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