utilitime

A small pure-python package for time-related utility functions.


Keywords
python, decorator, decorators
License
MIT
Install
pip install utilitime==0.0.3

Documentation

utilitime

PyPI-Status PyPI-Versions Build-Status Codecov LICENCE

A small pure-python package for utility decorators.

from utilitime.datetime import datetime_to_dateint
from utilitime.dateint import shift_dateint, today_int, dateint_range

start_dateint = datetime_to_dateint(some_dt_obj)
day_after_tomorrow = shift_dateint(today_int, 2)
dateints_to_process = dateint_range(start_dateint, day_after_tomorrow)

1   Installation

Install utilitime with:

pip install utilitime

2   Components

The package is composed of several sub-components, each dedicated to functions dealing with mainly one type of time representation.

2.1   dateint

The dateint format uses integer objects to decipt a specific calendaric day; e.g. 20161225. This components deals with converintg and transforming objects of this type.

2.2   datetime

Utilitu methods for standard dateime.dateime objects.

2.3   time

Utilitu methods for standard dateime.time objects.

2.4   timestamp

The timestamp format uses integer objects to decipt a specific moment in time by seconds (or sometimes milliseconds) since the epoc;h e.g. 1506984924. This components deals with converintg and transforming objects of this type.

2.5   weekday

Utility methods for ordered lists of weekday names.

2.6   TimeInterval

Defines a type corresponding to a time interval between two specific points in time (and not their difference, like datetime.timedelta).

3   Contributing

Package author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.

3.1   Installing for development

Clone:

git clone git@github.com:shaypal5/utilitime.git

Install in development mode with test dependencies:

cd utilitime
pip install -e ".[test]"

3.2   Running the tests

To run the tests, use:

python -m pytest --cov=utilitime

3.3   Adding documentation

This project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please follow these conventions.

4   Credits

Created by Shay Palachy (shay.palachy@gmail.com).